Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Torque Ripple Suppression Strategy of PMSM under Variable Speed Condition

Due to the influence of motor body factors such as air gap magnetic field distortion, as well as external motor factors such as inverter circuit dead time and on-voltage drop, the stator phase current of the permanent magnet synchronous motor (PMSM) under traditional control strategy has harmonics, which leads to current distortion. A large number of current harmonics cause torque ripple of PMSM during operation, which affects the motor performance. To solve this problem, this paper proposes an effective suppression control strategy by voltage compensation. Firstly, the harmonic mathematical model of PMSM was established and the harmonic disturbance was analyzed. The results show that the specific harmonic current will affect the operation of PMSM. Then, the harmonic suppression loop was added to the PMSM vector control system. This paper proposed a novel harmonic current extraction method, which can extract the total harmonic current based on the real-time PMSM speed. The harmonic current and torque ripple can be suppressed by compensating the appropriate voltage to the control system. Finally, the simulation experiment was carried out under Matlab /Simulink software. By analyzing the PMSM current waveform and electromagnetic torque waveform with or without harmonic suppression strategy, the effectiveness of the control strategy was verified.
A Novel Weakly Supervised Framework Based On Noisy-Label Learning For Medical Image Segmentation

Obtaining accurately annotated medical images for training segmentation models is expensive, time-consuming and labor-intensive. Although a variety of approaches based on weak annotations like points, scribbles and bounding boxes have been designed to address this problem, their performance is still limited. Inspired by recent studies on noisy-label learning, we propose a novel two-stage framework where a size-constrained loss is used to directly learn from the weak annotations in the first stage and a noise-robust loss is introduced to learn from pseudo labels in the second stage. The noise-robust loss function, named Edge-Dice, is based on the confidence in the network’s prediction and the pseudo labels. Our approach differs from previous works by taking a natural step towards stronger supervision, in which predictions made by weak supervision methods are incorporated into another round of training using noise-robust methods. Experiments with the ACDC 2017 dataset showed that our method achieved 86.27% Dice for left ventricular segmentation with only 1% of the full annotations, and it outperformed existing methods with the same set of weak annotations.
Effective speech emotion recognition using deep learning approaches for Algerian dialect

Speech Emotion Recognition is an active research problem in the field of deep learning. Where the goal is to detect different emotions within audio speech using deep artificial neural networks. In this work, we focus on speech emotion recognition in the Algerian dialect. The paper introduces a new large Algerian speech emotion dataset collected from different Algerian TV shows. The audio signals were manually annotated as happy, angry, neutral, or sad. After the data collection, we applied several classification methods such as machine learning-based models, convolutional neural networks (CNNs), Long Short Term Memory (LSTM) networks, and Bidirectional LSTM (BLSTM). After applying the proposed models on our dataset, the best classification accuracy is 93.34% achieved by the LSTM-CNN model.
Design and development of a biosensor for uric acid detection based on self-assembled monolayers (SAMs) made on screen-printed gold electrodes.

This work presents the development of an enzymatic biosensor for uric acid (UA) detection based on the immobilization of uricase (Uox) on cysteamine (CYS) self-assembled monolayers (SAM). The SAMs were created on the surface of a screen-printed gold electrode (AuSPE) modified with gold nanoparticles (AuNPs) to allow the Uox immobilization using crosslinking. To verify the surface modifications, the electrodes were characterized by scanning electron microscopy (SEM), X-ray energy dispersive spectrometry (EDS) and cyclic voltammetry. The structural techniques corroborated the SAMS formation over the AuNPs electrode and the improved electrochemical performance after all the modifications. Finally, the assembled biosensor was evaluated against UA showing a wide sensing range from 1.68 to 336.20 mg/dL, a sensitivity of 0.6607 with an R2=0.9632. The device also showed good selectivity against interference species like ascorbic acid. The biosensor showed promising results as an alternative method for fast, sensitive, and straightforward uric acid detection.
Simulation of Astrocytic Calcium Dynamics in Lattice Light Sheet Microscopy Images

Astrocytes regulate neuronal information processing through a variety of spatio-temporal calcium signals. Recent advances in calcium imaging have started to shine light on astrocytic activity, but the complexity and size of the recorded data strongly call for more advanced computational analysis tools. Their development is currently hindered by the lack of reliable, labeled annotations that are essential for the evaluation of algorithms and the training of learning-based methods. To solve this labeling problem, we have designed a generator of 2D/3D lattice light sheet microscopy (LLSM) sequences which realistically depict the calcium dynamics of astrocytes. By closely modeling calcium kinetics in real astrocytic ramifications, the generated datasets open the door for the deployment of convolutional neural networks in LLSM.
Dynamic Functional Connectivity For The Classification Of Multiple Sclerosis Phenotype: A Hidden Markov Model Approach

We present a pipeline for the classification of subjects according to multiple sclerosis phenotype. The approach is based on a hidden Markov model built on dynamic functional connectivity. More in detail, a sequence of correlation matrices is built from the fMRI time slices, then projected on the tangent Euclidean space. The number of considered dimensions is reduced through PCA, then dominant set clustering is applied to group similar correlation matrices in a limited number of ”mental” states, which are then used as the hidden states of the Markov model. Subjects in the test set are then classified according to the likelihood of their sequence of observations with the obtained class-wise Markov models. We demonstrate that our approach is capable of discriminating multiple sclerosis phenotypes and that the persistence of some of the identified states is a possible neurohpyisiological marker of disease severity.
Stacked Pointnets For Alignment Of Particles With Cylindrical Symmetry In Single Molecule Localization Microscopy

Single molecule localization microscopy is an essential observation tool in biology that yields data in the form of point clouds. It is still limited by an anisotropic resolution and inhomogeneous labeling density. This issue can be addressed by reconstructing a single model from multiple aligned copies of the same particle. However, generic registration methods fail to align point clouds in the presence of anisotropic noise and outliers. Therefore, we propose an alignment method dedicated to a common type of particle geometry, namely cylindrical symmetry. We focus on the centriole, a fundamental macromolecular assembly with ninefold cylindrical symmetry. We design a neural network based on stacked PointNet architectures that estimates the center and axis of symmetry of individual particles in SMLM, in order to align them in the same canonical space. We demonstrate the robustness of our approach on simulated and real dSTORM data.
Depicting a Neural Model for Lemmatization and POS Tagging of Words from Palaeographic Stone Inscriptions

Lemmatization is essential before POS (Part-of-Speech) Tagging for analysis of morphology and the removal of inflections by returning the base of the word without the endings. POS is to indicate tagging the words into categories of grammatical terms in analysis of text and marking up linguistic words in a script. Considering the combinations and inflections in the words of Tamil language, there is difficulty in Lemmatization and POS Tagging classification and prediction of Tags of the words. As the automated tools are very rare for modern Tamil language there is a lack of such statistical methods and techniques for the Paleographic Tamil language such as the texts from inscriptions of stone where the words are combined, staked, overlapped and compounded without splitting up into morphemes or lemmas. The proposed work overcomes the complexity of splitting up and classifying ancient words. The proposed work is based on designing the Neural Model for POS Tag Classification and Prediction of Words from the Paleographic 11th century stone inscription script. Bi-LSTM model is implemented with the embedding layer of vectors of words for training the POS Tagging model and classifying the words into tags and prediction of Tags of words for any novel script given that involves syntactic tag assigning and predicting tag for concerning words efficiently. The proposed model provides 96.43% accuracy compared to the existing works in the stream.
Testing Virtual Network Functions Auto-Scaling using Open-Source Management and Orchestration

Recently, the open-source management and orchestration (OSM) platform has attracted significant interest from academia and industry, potentially providing network function virtualization (NFV) management and orchestration solutions for European Telecommunications Standards Institute (ETSI) compliant NFV implementations. OSM has a built-in monitoring module (MON), a policy-management module (POL), and a Prometheus time-series database for metrics collection, which are the main components of ensuring service assurance in OSM. In this work, auto-scaling testing is performed with OSM after instantiating network service using the OSM community-developed virtual network functions (VNFs) and the Grafana tool for data visualization. The Prometheus database collects the metrics from the MON exporter, and scaling actions are defined in POL. Finally, we performed auto-scaling testing and demonstrated the results using the Grafana dashboard. The results showed that the auto-scaling policy was automated successfully after the virtual infrastructure manager (VIM) and VNF metrics triggered threshold violation alarms (already defined) and scaling actions were performed successfully as defined in POL.
Willingness to Use Self-Service Technologies Innovation on Omnichannel

Omnichannel includes innovations in Self Services technologies such as Augmented Reality, Virtual Reality, Amazon-Whole Foods, Amazon Go, Amazon Go Grocery, etc. This paper aims to develop a conceptual framework using ECM and UTAUT2 of customer willingness to use Self Services Technologies. In the end, this will provide insight into the factors that initially influence the willingness to use Self Services Technologies Innovation on Omnichannel. This conceptual framework contains eight potential constructs and expected two moderating variables to help develop and analyze a model of willingness to use Self-Service Technology Innovation using structural equation modeling.
Mobi-Track: Distilling Direct Path in Time Domain for High Accuracy WiFi Tracking

Thanks to the ability to decompose multipath from raw data, decimeter-level localization and tracking solutions have come to reality. Yet, one daunting task in such solutions is how to robustly and efficiently identify the direct path from multiple candidates. In this paper, we present Mobi-Track to explore the mobility of target to handle this challenge and output the trajectory precisely. Through a novel likelihood function, Mobi-Track can distill the direct-path with high probability. We further propose a Forward Pruning algorithm to reduce the computational burden and make it more applicable in practice. A prototype is implemented based on commercial WiFi NICs. Our extensive experiments show that Mobi-Track can achieve AOA error of about 16.1° in Line-of-Sight (LOS) scenario and 21.7° in Non-Line-of-Sight scenario, and a median tracking error of 0.86 m in LOS scenario, making Mobi-Track an excellent option for a wide range of indoor localization and tracking applications to significantly boost their performance.
A Novel Framework for Accurate and Non-Invasive Pulmonary Nodule Diagnosis by Integrating Texture and Contour Descriptors

An accurate computer aided diagnostic (CAD) system is very significant and critical for early detection of lung cancer. A new framework for lung nodule classification is proposed in this paper using different imaging markers from one computed tomography (CT) scan. Texture and shape features are combined together to show the main discriminative characteristics between malignant and benign pulmonary nodules. 7th-Order Markov Gibbs random field, (MGRF), is implemented to give a good description of the nodule’s appearance by involving the spatial data. A Various-views Marginal Aggregation Curvature Scale Space (MACSS) and the primitive geometrical properties are used to indicate the nodule’s shape complexity. Eventually, all these modeled descriptors are combined using a stacked autoencoder and softmax classifier to give the final diagnosis. Our system has been validated using 727 samples from the Lung Image Database Consortium. Our diagnosis framework’s accuracy, sensitivity, and specificity were 94.63%, 93.86%, 94.78% respectively, showing that our system serves as an important clinical assistive tool.
Improving Land Use Classification using Human Mobility-based Hierarchical Place Embeddings

Understanding land use patterns is becoming increasingly important for effective urban planning. Meanwhile, place embeddings, which are generated from human mobility data collected from mobile devices, have become a popular method to understand the functionality of places, and are essential for various downstream tasks including land use classification and mobility prediction. Place embeddings with high spatial resolution are desirable for land use classification, however, downscaling the spatial resolution could deteriorate the quality of embeddings due to data sparsity, especially in less populated areas. We address this issue by proposing a method that is able to generate fine grained place embeddings, by leveraging spatial hierarchical information according to the local density of observed data points. We demonstrate the practical value of the generated fine grained place embeddings to better understand land use, using real world trajectory data from 3 cities in Japan and comparing the proposed method with the baseline non-hierarchical method. Our technique of incorporating spatial hierarchical information can complement and reinforce various place embedding generation methods.
Detection and Classification of Conflict Flows in SDN Using Machine Learning Algorithms

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows.
Research on Active Disturbance Rejection Control of Active Magnetic Bearing-Rotor System

Aiming at the problem that the control effect of PID-controlled active magnetic bearing (AMB)-rotor system cannot meet technical requirements of high-speed motors, this paper proposes to apply active disturbance rejection control (ADRC) to the AMB-rotor system. On the basis of introducing the structure of the radial four-degree-of-freedom (DOF) AMB, the dynamic model of the AMB-rotor system is established, and the ADRC for the single-DOF rotor system and the active disturbance rejection decoupling control for the four-DOF rotor system are proposed. According to the parameters of the prototype, a single-DOF ADRC simulation system and a four-DOF ADRC simulation system are constructed, the model established by Simulink compares the response performance of PID and ADRC under step response and the anti-interference ability of sudden interference. It can be seen from the simulation results that the AMB-rotor system controlled by ADRC can achieve stable suspension operation, with better stability, faster response, and stronger robustness
Structural And Functional Interplay In Anxiety Related Classification: A Graph Signal Processing Approach

Anxiety disorders are one of the most common mental health conditions with a high rate of everyday life disability. Connectivity is steadily gaining relevance to increase our knowledge of psychiatric diseases. Graph signal processing (GSP) is a new framework to integrate structural connectivity and brain function. We propose here a graph-based analysis using GSP metrics and classification procedure, to identify anxiety biomarkers. Results suggest that the joint consideration of structure-function features improves their discriminatory accuracy, and our understanding of the pathophysiology of anxiety.
Targeting Patterns of Driving Characteristics in Testing Autonomous Driving Systems

A common approach in testing automated and autonomous driving systems (ADS) consists in running the ADS in a simulator where driving and environmental conditions are specified in terms of scenarios. An important aspect in ADS testing is to cover different driving situations in which the autonomous car must perform different types of maneuvers. In this paper, we consider the path planner of our industry partner; the path planner is responsible for deciding the path that must be followed by the autonomous car. A path is characterized by the driving characteristics (as forward acceleration, lateral acceleration, curvature, and so on) that are needed, at each time point, to implement it. For different driving characteristics, a good test suite should contain a scenario for which the path planner chooses a path that requires the application of the selected driving characteristics for a non-negligible period of time: this means that the characteristics are relevant in that path. With such a test suite, engineers can observe the different types of decision taken by the path planner, and so possibly better assess its correctness. In the paper, we introduce the notion of patterns of driving characteristics, to characterize their interaction (i.e., simultaneous or not) and measure their duration. Exploiting this definition, we propose two search-based approaches (for single and pairs of driving characteristics) to find scenarios in which such patterns occur and their duration is maximized. Experimental results show that the approaches are effective in finding scenarios for which the path planner generates paths where the different driving characteristics occur in terms of the specified pattern.
Comparative Studies On Drug-Target Interaction Prediction Using Machine Learning and Deep Learning Methods With Different Molecular Descriptors

New drugs introduced in the market should not only be able to cure the targeted indications, but also should have no or minimal side effects to our body. Traditional drug discovery including in-vitro and in-vivo methods is a challenging, costly and time-consuming process. Thus, in-silico method was introduced where the Drug-Target Interaction (DTI) is predicted using computation methods. A reliable DTI prediction model would save a lot of time and costs for drug discovery since not all drug candidates but only the actives candidates would be further tested in the wet labs. In this paper, various binary classifier models have been developed to predict the activeness or inactiveness of compounds (drugs) from ChEMBL database against 11 activity classes (targets). The prediction methods used in this study are Support Vector Machine (SVM), Naive Bayes (NB), Feed-forward Sequential Model (SEQ) and Convolutional Neural Networks (CNN). The molecular descriptors used to convert the SMILES chemical notation into binary fingerprint are Extended Connectivity Fingerprints 4 (ECFP4) and Maccs Key (MACCS). The results shows that the 11 predictive models have potential to be applied in real-world situation to predict the interaction of compounds against 11 target proteins as it is found that most machine learning and deep learning methods works better with data represented in ECFP4 compared to MACCS molecular descriptor.
Software Defined Networking Failure Recovery with Flow Table Aware and flows classification

Software Defined Networking (SDN) emerged to simplify network management and speed up innovation through a clear separation of the control plane from the data plane using OpenFlow standard. Link or switch failure occurs more often. The detect switch has to send a port status message to the controller to update the Flow Table with a new path or switch to a preconfigured backup path to recover from the failure. Most of the existing schemes are focusing on distance or delay in backup path selection. However, traffic flows exhibit variabilities and losses caused by flows also differ with respect to Flow Table utilization. Ternary Content Addressable Memory (TCAM) that stores flow entries in the Flow Table is costly with limited space. It is significant to reduce backup path entries resource consumption through different failure recovery strategies while considering the Flow Table utilization ratio at sampling time, flow classification, and shortest path. This paper devised an algorithm for backup path computation based on the shortest path, flow classification, and Flow Table utilization ratio. The simulation result shows higher throughput of (71.2), lower packet losses (3 %), and round-trip time (3.4 s).