Miracle-dz/UniFAR-FTUs huggingface dataset:
UDE-Based Robust Bipartite Consensus Control of Multi-agent Systems with External Disturbances

This article studies the bipartite consensus control problem of multi-agent systems with external disturbances. A robust distributed control scheme is proposed to achieve bipartite consensus in which the convergence values of agents have the same modulus but different signs. To significantly attenuate the effect of external disturbances of multi-agent systems, an uncertainty and disturbance estimator (UDE) is designed to estimate the unknown disturbances based on the information of inverse system of the model. By constructing the Lyapunov function, the influence on the coupling of nearest- neighbor rule based controller and UDE compensation component on the system consensus stability is reduced. Finally, some simulations are designed to verify the effectiveness of the robust bipartite consensus control scheme.
The Detection of Parkinson's Disease From Speech Using Voice Source Information

Developing automatic methods to detect Parkinson's disease (PD) from speech has attracted increasing interest as these techniques can potentially be used in telemonitoring health applications. This article studies the utilization of voice source information in the detection of PD using two classifier architectures: traditional pipeline approach and end-to-end approach. The former consists of feature extraction and classifier stages. In feature extraction, the baseline acoustic features—consisting of articulation, phonation, and prosody features—were computed and voice source information was extracted using glottal features that were estimated by iterative adaptive inverse filtering (IAIF) and quasi-closed phase (QCP) glottal inverse filtering methods. Support vector machine classifiers were developed utilizing the baseline and glottal features extracted from every speech utterance and the corresponding healthy/PD labels. The end-to-end approach uses deep learning models which were trained using both raw speech waveforms and raw voice source waveforms. In the latter, two glottal inverse filtering methods (IAIF and QCP) and zero frequency filtering method were utilized. The deep learning architecture consists of a combination of convolutional layers followed by a multilayer perceptron. Experiments were performed using PC-GITA speech database. From the traditional pipeline systems, the highest classification accuracy (67.93%) was given by combination of baseline and QCP-based glottal features. From the end-to-end-systems, the highest accuracy (68.56%) was given by the system trained using QCP-based glottal flow signals. Even though classification accuracies were modest for all systems, the study is encouraging as the extraction of voice source information was found to be most effective in both approaches.
Device design to achieve low loss and high short-circuit capability for SiC Trench MOSFET

The trade-off between low on-resistance and high short-circuit (SC) capability crucial for silicon carbide metal-oxide-semiconductor (MOS) field-effect transistors. To break this trade-off, we clarified the design parameters that are key in improving SC capability and confirmed that our trench-etched double-diffused MOS (TED-MOS) can be suitably designed using its unique parameters to achieve low on-resistance while maintaining high SC capability.
Attentional Markov Model for Human Mobility Prediction

Accurate human mobility prediction is important for many applications in wireless networks, including intelligent content caching and prefetching, network optimization, etc. However, modeling mobility patterns has been a challenging problem due to the complicated human mobility patterns influenced by the long-term correlation with historical trajectories and context information, and the long time interval between consecutive mobility records. In this paper, we integrate the novel attention technique into the Markov model to predict future locations. This model allows us to consider the time context of users’ mobility behavior, which overcomes the disadvantage of the traditional Markov model in modeling the periodicity of trajectories. We conducted extensive evaluations using two different mobility datasets, which involve over 20,000 users. Our evaluations show that our proposed solution outperforms the state-of-the-art algorithms by over 6.6% on average. In addition, compared with the method based on deep recurrent neural networks achieving the same performance, our proposed model runs significantly faster, i.e., reducing the computation time by over 170 times, demonstrating the effeteness of our proposed model.
Blockchain-based Volunteer Edge Cloud for IoT Applications

Cloud computing has been widely used in the field of information services. However, large-scale Internet of things (IoT) applications are raising new challenges to cloud computing architecture. Edge computing, which complements cloud computing, is considered to be the way to address these challenges. Volunteer computing, which harvests idle resources in the network can improve the hardware utilization rate and support tens of billions of IoT devices. In view of the limitations of traditional volunteer computing that cannot provide realtime services and has no mechanism to reward services in existing volunteer clouds, this paper presents blockchain-based volunteer edge cloud. A common runtime environment is provided by container technology, and blockchain smart contract is used for critical business steps and computing service payment. Volunteer edge cloud systems based on blockchain is introduced from a top-level perspective, and a prototype system build on Ethereum and KubeEdge is described in detail. On top of the prototype system, we deployed an example IoT application of robot formation control. It demonstrates the benefits of volunteer edge cloud in reducing the complexity of IoT devices, improving the flexibility of software development, and paying the computing service.
Thermal 3D Modeling and Analysis of PMSMs with Inter-Turn Faults

Intern-turn faults are one of the most common causes of machine failure. The propagation of this fault is mainly determined by the local fault temperature. In this paper, we present a 3D analytical approach to determine the temperature distribution inside the windings during an inter-turn fault. To do so, a coupled electromagnetic-thermal model is used. The fault loss is calculated in the electromagnetic model and serves as an input for the thermal model. The thermal model consists of an extended layer approach, which we adapted to simulate inter-turn faults. It can be seen that the fault's location and the fault area have a major influence on the maximum temperature rise. For a fault in the active region close to the stator yoke the maximum temperature rises for a fault loss of 10 W by 8.8 K, whereas the maximum temperature rises by 137.6 K in the middle of the end winding. The maximum temperature rise is highly dependent on the fault's location and the maximum temperature rise increases, the closer to the air gap the fault occurs. The closer it is in axial direction to the middle of the end windings, the larger the maximum temperature rise.
Data Augmentation Methods and their Effects on Long-Range Dependence

Data augmentation is a common method for expanding datasets to train machine learning models. In this paper, five different methods are used to obtain augmented sets. In addition, eight measures are used for experimental evaluation of datasets before and after data augmentation methods. The key requirement is that any data augmentation should not alter the fundamental properties and characteristics of the original dataset. This research shows how some data augmentation methods can destroy the long-range dependence of the Internet traffic data (ITD) with distributed denial of service (DDoS) attacks (DDoS ITD), and consequently alter the probability mass function (PMF) and data labelling (tagging) of the DDoS ITD.
Hybrid Classification for High-Speed and High-Accuracy Network Intrusion Detection System

Cybercrime is growing at a rapid pace, and its techniques are becoming more sophisticated. In order to actively cope with such threats, new approaches based on machine learning and requiring less administrator intervention have been proposed, but there are still many technical difficulties in detecting security attacks in real time. To solve this problem, we propose a new machine learning-based real-time intrusion detection algorithm. Unlike the existing approaches, the one proposed can detect the presence of an attack every time a packet is received, enabling real-time detection. In addition, our algorithm effectively reduces the system load, which may significantly increase from real-time detection, compared to non-real-time detection. In the algorithm, the increase in the number of memory accesses can be minimized (to below 30 %) compared to conventional methods. Since the proposed method is pure software-based approach, it has excellent scalability and flexibility against various attacks. Therefore, the proposed method cannot support the high classification performance of the hardware-based method but also the high flexibility of the software-based method simultaneously, it can effectively detect and prevent various cyber-attacks.
Automated Text line Segmentation and Table detection for Pre-Printed Document Image Analysis Systems

Document Image Processing is considered one of the widely used technologies today. It has proved to be extremely useful in digitizing physical documents to preserve them for longer times. The primary challenge in this work is to extract the overlapping lines from handwritten document images and table detection from pre-printed documents. In this research paper, we present a line segmentation and table detection algorithm suitable for pre-printed and handwritten documents. Initially, the input document image is preprocessed and a baseline is created for offline every peak to carry out line segmentation. Further, table detection is accomplished with the use of edge detection techniques and morphological operations
Constrained learning for Multicell Power Control

This paper proposes a deep neural network (DNN) based method to solve the multicell power control problem that maximizes the sum rate subject to per-user rate constraints. The basic idea is to employ a two-DNN concatenating network structure, where the second DNN associated with a randomization processing is designed to guarantee the per-user rate constraints via supervised learning, given which the first DNN is trained to directly maximize the sum rate by unsupervised learning. Simulation results demonstrate that the proposed method can achieve better performance with low complexity compared to existing deep learning and numerical optimization methods.
p-NiO Junction Termination Extensions for High Voltage Vertical GaN Devices

In this paper, we illustrate the use of RF-sputtered p-NiO to fabricate Junction Termination Extensions (JTEs) for high-performance vertical GaN-on-Si Schottky Barrier Diodes (SBDs). The GaN epitaxial structures were grown on 6-inch Si wafers by MOCVD. The fabricated SBDs with p-NiO JTEs exhibited a low specific on-state resistance of 3 mΩcm2 (20% lower than in reference SBDs without JTEs) and their breakdown voltage was improved to 433 V, the highest value ever reported for GaN-on-Si SBDs. Our work shows the great potential of p-NiO to achieve highly effective players for use in vertical GaN power devices.
Automatic 3-D Imaging and Measurement of Human Spines With a Robotic Ultrasound System

Comparing with X-ray, ultrasound imaging has the advantages of no radiation and easy operation, etc., which is expected to be an effective alternative in clinical assessment for the human spine. At present, freehand scanning is mainly used in ultrasound imaging field, and recently robot-assisted scanning has emerged as a promising way to gradually reduce human intervention. However, the automatic recognition and scanning, as well as the control strategy of robots that guarantee the tight coupling between a probe and human skin, are still challenges. Moreover, the current measurement method of the spine is usually conducted based on plain radiographs, which lacks spatial structure information of spine. In this article, a new radiation-free robotic ultrasound system for 3-D imaging and a measurement method of the spine based on 3-D imaging are presented. A fully convolutional network (FCN) named fuse-Unet based on RGB and depth images captured by an RGB-depth (RGB-D) sensor Kinect, is proposed to realize the automatic recognition of human spine area and scanning path pre-planning. Then a six-degree-of-freedom robotic arm plays the role of a doctor to complete the automatic scanning along the pre-planning path, during which a normal-vector-based method and two force sensors are used to ensure that the probe well fits the spine area. Finally, 3-D ultrasound reconstruction and visualization of the spine are realized, based on which cobb angles are calculated for assessing the morphological structure of the spine. Phantom and in vivo experiments are conducted for evaluating the performance of the proposed system. Experimental results validate the practicability and accuracy of the system, indicating its potential for clinical application.
Speech Emotion Recognition Using ANN on MFCC Features

Speech Emotion Recognition (SER) is one of the active research topics in Human-Computer Interaction. This paper focuses on training an ANN Model for SER using Mel Frequency Cepstral Coefficients (MFCCs) feature extraction and training it on selected audio datasets to compare the performance. The model can classify audio files based on a total of eight emotional states: happy, sad, angry, surprise, disgust, calm and neutral, although the number of emotions varies in selected datasets. The proposed model gives an average accuracy of 99.52% on the TESS data set, 88.72% on the RAVDESS data set, 71.69% on the CREMA data set, and 86.80% on the SAVEE data set.
Selected Harmonic Elimination Pulse Width Modulation for Open-end Winding Motors with a Common DC link

This paper presents an implementation method for selective harmonic elimination pulse width modulation for dual inverters feeding an open-end winding motor. The modulation method is intended for dual inverters with a common DC link, where the zero sequence current must be suppressed by proper modulation. Selective harmonic elimination is highly effective in providing improved power quality at low apparent switching frequency, which makes the proposed modulation appealing especially for high-speed drive applications. The proposed modulation method and carrier based pulse width modulation were simulated in a high-speed equivalent circuit. The simulation study focuses on using a static load to present power quality produced by the modulators without load or control interactions. It was found that the proposed method was able to shift the harmonic peaks of the load current from the apparent switching frequency to a higher band and to improve the load current quality.
Basic User Interaction Features for Human-Following Cargo Robot TIAGo Base

The ability to follow a human is necessary for many types of service and social robots. A development of human-following robots requires taking into account the features of a perception of such robots by users. This paper presents results of human-robot interaction experiments with TIAGo Base cargo robot when it follows a user. Eight people took part in the experiments. The participants interacted with the robot using voice commands. TIAGo Base tracked a person using a single laser rangefinder. The purpose of the experiments was to identify the features of a user interaction with TIAGo Base robot through voice commands when the robot carries a load and follows the user. In addition, we identified a preferable distance between TIAGo Base robot and a user.
A Bootstrap Voltage Clamping Circuit for Dynamic VTH Characterization in Schottky-Type p-GaN Gate Power HEMT

A multi-functional bootstrap voltage clamping circuit is proposed for characterizing the drain-bias-induced dynamic VTH, in which the GaN HEMT serves as the key bootstrapping device. This new testing setup covers a wide range of OFF-state drain bias (VDSQ) up to the voltage rating of GaN power HEMT, a short OFF-to-ON (or stress-to-sense) delay (Tdelay) of 60ns, a high switching frequency (fsw) up to 2MHz, and a wide OFF-state static stress time (Tstress) from 0. 5µs to 399s. The dynamics of the threshold voltage (VTH) shift in 650-V Schottky-type p-GaN gate HEMT under short- and long-term static stress conditions and temperature-dependent dynamic VTH characteristics are systematically investigated using the proposed circuit.
Lightweight Oriented Object Detection Using Multiscale Context and Enhanced Channel Attention in Remote Sensing Images

Object detection is a focal point in remote sensing applications. Remote sensing images typically contain a large number of small objects and a wide range of orientations across objects. This results in great challenges to small object detection approaches based on remote sensing images. Methods directly employ channel relations with equal weights to construct information features leads to inadequate feature representation in complex image small object detection tasks. Multiscale detection methods improve the speed and accuracy of detection, while small objects themselves contain limited information, and the features are easily lost following down-sampling. During the detection, the feature images are independent across scales, resulting in a discontinuity at the detection scale. In this article, we propose the multiscale context and enhanced channel attention (MSCCA) model. MSCCA employs PeleeNet as the backbone network. In particular, the feature image channel attention is enhanced and the multiscale context information is fused with multiscale detection methods to improve the characterization ability of the convolutional neural network. The proposed MSCCA method is evaluated on two real datasets. Results show that for 512 × 512 input images, MSCCA was able to achieve 80.4% and 94.4% mAP on the DOTA and NWPU VHR-10, respectively. Meanwhile, the model size of MSCCA is 21% smaller than that of its predecessor. MSCCA can be considered as a practical lightweight oriented object detection model in remote sensing images.
Design of Dual-Frequency MIMO Antenna with High Isolation Based on Odd-Even Mode Analysis Method and Defected Ground Structure

This paper proposes a MIMO antenna that can work in two frequency bands of 2. 45GHz and 5. 8GHz. For this antenna structure, two decoupling methods are designed to improve the isolation of the two working frequency bands of the MIMO antenna In this way, the influence between multiple ports can be reduced and the normal operation of the antenna can be guaranteed. For the 2. 45GHz frequency band, the odd-even mode analysis method is adopted to achieve the effect of improving the isolation through impedance matching under the odd-even mode excitation. The process is mainly through theoretical calculation and analysis to obtain a decoupling structure, avoiding the tedious structural optimization process. The actual antenna experiment shows that S12 is reduced by 9dB. For the frequency band of 5. 8GHz, the E-shaped defected ground structure is selected, and S12 is reduced by 13dB without affecting the working effect of the low frequency band.
A Survey on Chatbot: Futuristic Conversational Agent for User Interaction

A chatbot aspires to make the interaction between mankind and machine through acoustic and textual techniques. There are enormous types of speech-based assistants. Few of them are Siri (developed by Apple), Google Assistant, Microsoft’s Cortana, and Amazon’s Alexa. NLTK for Python, which is a Natural Language Processing (NLP) technique, is pragmatic to analyze speech as well as smart replies or responses that appear by designing an engine to deliver responses as a human does. For a chatbot to accurately excel a dialogue of a human, it should anatomize the input provided by a user appropriately and articulate a relevant and appropriate response. This paper describes a survey on the strategies used to design chatbot and comparison that is prepared by examining different design techniques to the main ones adopted. The survey is done on the researches carried out in representing the significant prolongation in chatbot over the last few decades.
Optimal Power Flow Problem Solution Through a Matheuristic Approach

The optimal power flow (OPF) problem is a widely studied subject in the literature that has been solved through classical and metaheuristic optimization techniques. Nowadays, significant advances in computational resources and commercial optimization solvers allow solving complex optimization problems by combining the best of both worlds in approaches that are known as matheuristics, however, in order to solve the OPF problem, matheuristic approaches have been little explored. In this regard, this paper presents a novel Variable Neighborhood Descent (VND) matheuristic approach to solve the OPF problem for large-scale systems. The proposed algorithm combines the classic OPF model and the VND heuristic algorithm. The OPF problem is formulated as a mixed-integer nonlinear programming (MINLP) model, in which the objective function aims to minimize the fuel generation costs, subject to the physical and operational constraints of the power system. The integer variables of this MINLP model represent the control of taps positions of the on-load tap changers and the reactive shunt compensation equipment. To validate the proposed methodology, 17 power systems of specialized literature were tested with sizes from 14 to 4661 buses, and the obtained solutions are compared with the solutions provided by the commercial optimization solver Knitro. Results show the superiority of the proposed matheuristic algorithm compared with Knitro to solve the MINLP-OPF model for large-scale systems.
A Comparative Analysis of Deep Neural Networks for Brain Tumor Detection

The technological advancement in the field of medical science for the detection, classification and identification of several diseases is making the diagnosis process easier and efficient at the same time, provides a helping hand for medical practitioners in saving life. Health experts are making use of these most advanced technological practices for reaching at conclusions in the area of health care. Brain tumor detection is one of the key major challenges in medical field. Early detection of tumor plays the most important role in fixing the most efficient treatment techniques for increasing the survival rate of patients. Manual detection of tumors for diagnosing cancer from data generated from clinical instruments is a time consuming task and the efficiency depends upon the radiologist. So through this paper, we are proposing methods for automating the detection process which can help the radiologist reaching at a faster conclusion in an efficient manner. We are proposing methods based on the pretrained network models like ResNet and its variants for brain tumor detection. The obtained results shows that ResNet-152 is the most efficient one among them for brain tumor detection and we can automate the process more effectively.
Fast On-line Inductance Estimation for Interior Permanent Magnet Synchronous Machines Based on the Modified Square-wave Injection

This paper describes a technique for on-line inductance estimation for an interior permanent magnet synchronous machine (IPMSM) based on a modified square wave voltage injection in the $d$ and $q$ axes. The inductances of an IPMSM can be estimated within each pulse width modulation (PWM) cycle without the knowledge of other parameters, such as the flux linkage, the stator resistance or even the rotor speed. What is more, it is also faster and need fewer computing resources compare with other recursive techniques. Simulation results under different scenarios show that the proposed method can accurately track the inductances not only during steady states, but also during transients.