Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Dictionary Learning with Block Diagonal Regularization for Image Classification

Sparse representation and dictionary learning have been successfully applied to encode dense data and facilitate image classification. Though existing dictionary learning methods achieve better performance than their counterparts, the class discriminative ability of learned dictionary is still limited. This paper proposes a novel supervised dictionary learning method based on the prior of the block diagonal phenomenon, i.e., each sample should be well reconstructed by the samples in the same class while poorly reconstructed by the samples in other class. Specifically, a block diagonal regularizer is imposed on the affinity matrix to enforce the sparse representation matrix to have an approximately block diagonal structure, which makes the learned dictionary more discriminative and suitable for classification tasks. Furthermore, we present an effective optimization strategy by combining the alternating minimization with the alternating direction method of multipliers (ADMM) for the proposed framework. Experimental results on six real-world datasets show that the proposed method is more effective than state-of-the-art dictionary learning methods.
Stability Evaluation of AC/DC Hybrid Microgrids Considering Bidirectional Power Flow Through the Interlinking Converters

The bidirectional power flow through the interlinking converter (IC), in ac/dc hybrid microgrids (HMGs) consisting of distributed generators (DGs) with droop controllers, plays an important role on the stability of such systems during islanding. This paper investigates the impact of the power flow direction on the small-signal stability of islanded droop-based HMGs. Firstly, a linearized state-space model of an HMG is developed. Secondly, eigenvalue analysis is carried out to realize the dominant modes, which are the rightmost eigenvalues. Thirdly, participation factor analysis is performed to identify the system and control parameters that effect stability the most. Lastly, sensitivity analysis is conducted to determine the critical droop gains and stability margin. It is observed from the eigenvalue and sensitivity analysis that the dominant modes of HMGs become more stable as more power flows from dc to ac subgrid. On the contrary, an increase in the power flow from ac to dc subgrid degrades the HMG stability. Additionally, the sensitivity of the dominant modes to changes in ac and dc droop gains is studied under bidirectional power flow through the IC to ascertain their impact on the stability margins. Finally, time-domain simulations, in MATLAB/Simulink, suggest that more generation on the dc subgrid would enhance the overall HMG stability margin during islanding.
Smart Inverter Communication Model and Impact of Cybersecurity Attack

Increasing penetration of inverter based distributed renewable energy resources poses many challenges for stable grid operation. Smart Inverters (SI) offers some potential support to the grid operation. SI can provide voltage or frequency support to the grid. To realize the full potential of these benefits SIs must operate in coordination with Distributed Energy Resource Management Systems (DERMS). The coordinated operation of SIs and DERMS can only realized through a standardized and interoperable communication. This paper presents IEC 61850 based communication model for SIs. Furthermore, the IEC 61850 message exchanges required for SI operation, i.e. defining and enabling different operating curves, is implemented in laboratory using IEC 61850 emulation tools. Furthermore, the vulnerability of SI standardized communication to cyberattacks and its impact on distribution performance is showcased.
Fake News detection Using Machine Learning

The phenomenon of Fake news is experiencing a rapid and growing progress with the evolution of the means of communication and Social media. Fake news detection is an emerging research area which is gaining big interest. It faces however some challenges due to the limited resources such as datasets and processing and analysing techniques. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system.
Price-Based Demand Response with Linear OPF

The present work proposes a new formulation for price-based DR augmented optimal power flow for social benefit maximization objective. The objective is to minimize power purchase cost for Distribution System Operator (DSO) and cost of energy consumption for DR participating house. A renewable supply demand ratio (RSDR) based pricing scheme has been developed with load data visualization and analysis. The formulation is linear, includes voltages and reactive power with Irregular polygon linearization of branch flow limits. The assumption over $r$/$x$ ratio is not taken, making linearization applicable to distribution systems. Tests for proposed problem formulation and pricing are performed over IEEE 33-Bus test system. The results obtained are being able to establish the applicability of the proposed method for analyzing the effect of flexibility and number of household participation in DR. A detailed analysis has been presented on load factor improvement, peak occurring hour and peak profiling for the DSO. These results also demonstrate a decoupling between load profile peak and profit of DSO along with the 100% renewable utilization. The simulation shows the price scheme applicability for the entire session and DR performance is not affected by intra-seasonal variations in the load.
Stability Enhancement of Cascaded Power Converters Using Parallel Virtual Impedance Via Output Impedance Shaping of the Source Converter

This work addresses the problem of negative incremental resistance behaviour that arises due to the impedance interactions between the power electronic converters that consists of an LC filter and a tightly regulated DC-DC buck converter. Failing to address this problem renders the cascaded system prone to instability. This paper introduces three different parallel virtual impedances, connected across the capacitor of the source side converter for restoring the stability and also for improving the dynamic performance of the load converter, during the external disturbances. The specific advantage of the proposed approach is that it requires the sensing of voltage across the LC filter rather than the current through the inductor in LC filter for designing the required virtual impedance. All these impedance configurations not only provide stability but also ensure a good dynamic response both across the LC filter and at the load converter's output voltage terminals. The response from the MATLAB/SIMULINK based simulations reveal the stability at steady state and the dynamic performance shows the improvement over a second order virtual impedance and an optimal method. Among the chosen impedance network configurations, the series RL network exhibits superior dynamic response to external disturbances. Analytical and simulation results of the work are included.
Fall Detection and Activity Recognition Using Human Skeleton Features

Human activity recognition has attracted the attention of researchers around the world. This is an interesting problem that can be addressed in different ways. Many approaches have been presented during the last years. These applications present solutions to recognize different kinds of activities such as if the person is walking, running, jumping, jogging, or falling, among others. Amongst all these activities, fall detection has special importance because it is a common dangerous event for people of all ages with a more negative impact on the elderly population. Usually, these applications use sensors to detect sudden changes in the movement of the person. These kinds of sensors can be embedded in smartphones, necklaces, or smart wristbands to make them “wearable” devices. The main inconvenience is that these devices have to be placed on the subjects’ bodies. This might be uncomfortable and is not always feasible because this type of sensor must be monitored constantly, and can not be used in open spaces with unknown people. In this way, fall detection from video camera images presents some advantages over the wearable sensor-based approaches. This paper presents a vision-based approach to fall detection and activity recognition. The main contribution of the proposed method is to detect falls only by using images from a standard video-camera without the need to use environmental sensors. It carries out the detection using human skeleton estimation for features extraction. The use of human skeleton detection opens the possibility for detecting not only falls but also different kind of activities for several subjects in the same scene. So this approach can be used in real environments, where a large number of people may be present at the same time. The method is evaluated with the UP-FALL public dataset and surpasses the performance of other fall detection and activities recognition systems that use that dataset.
Scalable Behavioral Authentication

A Behavioral Authentication (BA) system constructs a behavioral profile for a user and uses it to verify their identity claims. It is primarily used as a second factor in user authentication. A BA system starts with an initial database of user profiles, and uses a verification algorithm to accept or reject a verification request, that consists of a claimed identity and behavioral data by measuring the distance between the profile of the claimed identity and the presented behavioral data. As new users join the system, the size of profile database grows and for a user <inline-formula> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula>, the probability of other users with “close” profiles increases, and this results in an increase in the error probability of verification algorithm. We analyze this problem in distance-based verification systems, and introduce the notion of scalability of BA systems that requires the system error probability to remain (almost) unchanged as the size of the profile database grows. To achieve scalability, we propose personalization of verification algorithm. More specifically, for a user <inline-formula> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula> the verification algorithm considers a set of doppelgangers that consists of users that have “close” profiles to <inline-formula> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula>, and uses the set to form additional verification checks for verification requests against <inline-formula> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula>. We use extensive experiments to validate the above, including the increase in the verification error with the increase in the database size, and implement and evaluate our proposed scalable verification algorithm in reducing this error. To our knowledge, this is the first systematic treatment of scalability of BA systems. Our notion of scalability and the approach to achieving it are general and applicable to other distance-based authentication systems.
Developing an Architecture for IoT Interoperability in Healthcare: A Case Study of Real-time SpO2 Signal Monitoring and Analysis

The article presents a novel approach to address the interoperability issues in the Internet of Things (IoT) systems. Based on the IoT Reference Architecture, an IoT system architecture integrated with gateways and data converter solutions has been proposed to enhance the information exchange and data sharing across interconnected devices, networks and platforms.In addition, distributed open-source analytic platforms for real-time computational and streaming processing has been introduced to the proposed architecture. A case study of real-time oxygen saturation (SpO2) monitoring and analysis has been implemented to demonstrate the feasibility of the proposed architecture. The experimental results show the efficiency in terms of collecting and analyzing large volumes of data.
Cross-subject MEG Transfer Learning by Riemannian Manifold and Feature Subspace Alignment

Single-subject oriented analyses have capacity to discover the individual neurocognitive model in neuroscience researches. However, individual discrepancies and spatial instability impede the group level inference. In this paper, we focus on transfer learning methods to cope with magnetoencephalographic (MEG) decoding across subjects within which the individual differences exist. First, a general and effective framework allows us to extract features in Riemannian manifold. Then, an improved subspace alignment technique is proposed to adapt two different domains. We test our method on MEG decoding challenge and find that it outperforms most state of the art methods both in cross-subject validation and new testing data.
Sensorless Control based on Sliding Mode Observer for PMSM drive

An ideal sensorless drive gives accurate speed information and control of speed, ranging from zero to the highest for all operating conditions irrespective of the uncertainties or disturbances present. Mechanical sensors which are used for tracking rotor position makes the system costly and less reliable. Therefore in high-performance drives, sensorless control methods are used for estimation or observation of speed and position. As hall effect sensors are used for measuring the stator currents, its actual value and estimated value can be used for adaptation. Stator current are used to estimate the correct speed and error of the drive. By using a continuous switching function called sigmoid (in place of signum function), chattering is reduced considerably. A Lyapunov function is constructed to analyze the stability of the proposed SMO. Extensive simulations are carried out and an SMO based sensorless drive is proved to be robust against parameter fluctuations and uncertainties.
Multi-source Machine Learning for AQI Estimation

In many countries worldwide, effectively estimating AQI values and levels is essential for better monitoring the air pollution around the living area. This problem has become one of the interesting research subjects for many years, and there are many applications developed for personal usages. In this work, we aim to investigate a multi-source machine learning approach to approximate the local AQI scores at users’ location in a big city. We conduct different experiments on three primary data sets: "SEPHLA-MediaEval 2019", "MNR-Air-HCM," and "MNR-HCM," collected in Ho Chi Minh City (Vietnam) and Fukuoka city (Japan). From the data sets provided, we extract different types of useful attributes for the problem: the timestamp information, the geographical data, sensor data (humidity and temperature), users’ emotion tags (such as greenness, calmness, etc.), the semantic features from images captured by users as well as the public weather data (including temperature, dew point, humidity, wind speed, and pressure) of the related cities. After that, we compare five distinct machine learning models for estimating the local AQI score and level, including Support Vector Machine [1], Random Forest [2], Extreme Gradient Boosting [3], LightGBM [4] and CatBoost [5]. We use RMSE, MAE, and R2 for measuring the performance of these approaches. The experimental results show that using random forest with sensor data, combined with public weather data, the results in AQI values regression and AQI ranks prediction can be the highest in many cases.
Enhancement of Contour Smoothness by Substitution of Interpolated Sub-Pixel Points for Edge Pixels

This study designed a sub-pixel precision edge detecting algorithm to enhance contour smoothness. First, the coordinate value of RGB pixel is projected on the space line of R=G=B to obtain gray image. Then, pixel edges are located using a Canny detector. Next, the edge width is thinned to a single pixel using a morphological thinning operation. Finally, sub-pixel-level smooth contours are extracted by interpolation. In this sub-pixel level contour extraction process, a Single-Pixel-Multi-Point interpolation method was developed to enhance edge smoothness and obtain high precision in edge estimation. This method divides edges in a $3\times 3$ pixels block into nine arrangement modes. According to the arrangement of the eight neighborhoods of a centered edge pixel, different locations of interpolated sub-pixel points are calculated by interpolation with Bezier curves. For symmetrically arranged linear edge pixels, this method can be used to determine the exact contour. Experimental results showed that the proposed algorithm can improve the smoothness of image edge contour. As the curvature of the edge increases, the maximum systematic error will increase. For the edge pixel centered in the $3\times 3$ pixels block and two pixels located at the corner of one side, the max systematic error is 0.5 pixel. For two edge pixels aligned in a single row or column with one located at a corner, the max systematic error is 0.25 pixel.
On the Mining of the Minimal Set of Time Series Data Shapelets

Shapelets, also known as motifs, are time series sequences that have the property of discriminating between time series classes. Lately, shapelets studies have gained a lot of momentum due to their interpretable nature. As opposed to traditional time series classifiers, shapelet-based learners provide a visual representation of the pattern that triggers the classification decision. One of the most challenging issues of shapelet-based classifiers is the generation of a large number of shapelet outputs. To the best of our knowledge, this is the first effort that addresses the high numerosity problem of mined shapelets issue by mining the minimal set of discriminative shapelets for time series data. We propose a new shapelet mining learner, 1DCNN, that has the property of learning shapelets of different lengths using a black-box neural network model. 1DCNN optimizes the entire classification schema by learning the shapes of the representative patterns. Our proposed model uses network pruning to sparsify the network and keep only the most discriminative shapelets without compromising the classification accuracy. We validated our model using 59 real-world time series datasets from the UCR repository. Our experimental results show the effectiveness and efficiency of our approach in comparison with other competing baselines models. For fairness purposes, we did not compare 1DCNN with ensemble based approaches that encapsulates many learners. Our results show that the performance of our model is superior to all other baselines pertaining to the shapelet-based classifier category, with up to 95% less Floating Points Operations per Second (FLOPs) required by the network.
LD–LP Generation of Personalized Learning Path Based on Learning Diagnosis

With the rapid advancement of education, personalized learning has gained considerable attention in recent years. Learning path plays an important role in this area and has attracted great concern. Many generating mechanisms have been proposed from different perspectives for assisting learning. Some methods focus on learners’ interest, while some methods pay close attention to learning styles. This article proposes a new learning path generating method named learning diagnosis–learning path (LD–LP), which is based on knowledge structure and learning diagnosis. Different from the previous work, it takes knowledge relation, learner's ability, learning time, and repetition into account. The difficulty of knowledge provided to students will be adjusted according to students’ academic achievement and learning time adaptively. The system generates personalized learning path by the sequence of learning knowledge. Students who were majoring in mathematics were invited to participate in the experiment. Experimental result demonstrates that LD–LP works effectively and it has high adaptivity and positive feedback.
An Adaptive Bidirectional Multibeam High-Altitude Platforms Aeronautical Telecommunication Network Using Dual Concentric Conical Arrays

An efficient aeronautical telecommunication network (ATN) is proposed in this article based on adaptive multibeam antenna arrays and high-altitude platforms (HAPs). First, the network structure is demonstrated, and its geometry is analyzed based on a geocentric coordinate system that converts the global positioning system (GPS) and altitude data of aircrafts and HAPs into direction-of-arrival (DOA) information which is required for subsequent beamforming operation. The antenna array is then formed by designing a low-profile dual concentric conical array (DCCA) with uniform elements distribution and is used at the aircraft and HAP to achieve bidirectional beamforming. The antenna array elements are fed by an adaptive-exponent sine profile function to reduce the sidelobe levels while the low-profile dual conical array structure reduces the secondary major lobe and backlobe levels. The proposed beamforming technique is also capable of providing multibeam towards several aircrafts at the same time for effective resource sharing and management. The radiation performance of the array is demonstrated, analyzed, and compared with the concentric circular array where it is found that the secondary major lobe has been reduced by 13 dB with sidelobe level down to −40 dB relative to the mainlobe level. In addition, the bit energy-to-noise power spectral density and probability of bit error of the proposed aeronautical network has been investigated at different operating frequencies including 3.5 GHz and millimeter wave (mm-wave) frequencies of 28 and 39 GHz. The simulation results have shown that a channel capacity of about 3 Gbps can be achieved for BPSK and QPSK signals using bidirectional beamforming for a channel bandwidth of 400 MHz at 28 and 39 GHz mm-wave frequencies while it is about 773 Mbps at 3.5 GHz for 100 MHz bandwidth that can be provided over a line-of-sight distance of 896 km between an aircraft and HAP.
Millimeter-wave Wide-Angle Scanning Phased Array Based on Low-profile Wide-beam Patch Antenna Element

In order to enlarge the scanning range of planar phased arrays, a new low-profile wide-beam patch antenna element with bent metal pillars is proposed, where the 3-dB beamwidth in E-plane can be extend to be over 100°, and the profile is only 0. 076λ0. Meanwhile, by loading a stacked patch and a H-type feeding slot, its operating band covers from 24.5 to 29.3 GHz with a relative bandwidth of 17.68%. Based on the newly constructed wide-beam element, a 1×9 planar phased array is designed, and several metal rings are etched among the adjacent elements to ensure the high isolation. The results show that the main lobe of the proposed phased array can obtain a wide-angle scanning from −62° to 62° in the whole operating band, which would be potentially applied for future 5G millimeter-wave (mmW) communication.