Miracle-dz/UniFAR-FTUs huggingface dataset:
The Automatic Detection of Speech Disorders in Children: Challenges, Opportunities, and Preliminary Results

Given the limited accessibility to Speech and Language Pathologists (SLPs) children in need often have, pediatric Computer-Aided Speech Therapy (CAST) tools can play an important role in the early diagnosis and treatment of speech disorders. However, various challenges impede the implementation of accurate automated analysis of speech disorders in children. In this article, we first discuss three key challenges in processing child disordered speech: 1) the unreliability of low-level annotation and scarcity of speech corpora, 2) speaker diarization of therapy sessions and 3) inaccurate children's acoustic models. We next explore opportunities to overcome some of these challenges. First, we investigate the effectiveness of high-level paralinguistic features in disordered speech detection to reduce the dependency on annotated data. A binary classifier trained using paralinguistic features extracted from both typically developing children and those suffering from Speech Sound Disorders (SSD) achieved 87% subject-level classification accuracy. Second, we tackle the speech disorder detection problem as an anomaly detection problem where models are trained merely on typically developing speech, reducing the need for disordered training data. A phoneme-level F1 score of 0.77 was obtained from an anomaly detection-based system trained on speech attribute features to classify between typical and atypical phoneme pronunciations of children with speech disorder. Finally, we test the efficiency of an x-vector based speaker diarization technique in pediatric therapy sessions. The method successfully distinguished between therapist and child speech with a Diarization Error Rate (DER) of 10%.
Comparison between DSRC and other Short Range Wireless Communication Technologies

DSRC (also known as IEEE 802.11p or WAVE) is a medium/short-range RF communication technology designed specifically for in-vehicle environments that provide high-speed, real-time, accurate, and reliable connectivity between vehicles and vehicles, vehicles, and roadside infrastructure. The single/two-way voice, image and data communication services support vehicle public safety, traffic management, in-transit information release, commercial freight management and nonstop charging services, which can significantly improve the safe operation efficiency of road traffic. It consists of a series of protocols and standards that work very similar to RFID technology. This study presents a compares DSRC with several other commonly used short-range wireless access technologies in terms of transmission rate, spectrum characteristics, and communication modes.
Vision-based attitude estimation for spacecraft docking operation through deep learning algorithm

On-orbital services, especially in docking operation and other space object interaction. The missions need accurate, reliable, and robust detection to be an accurate in joining any interaction concerned. Two spacecrafts with an unknown mathematical model to predict the position and orientation, a computer vision-based attitude estimation system to detect the poses of spacecraft via camera is the key option of the mission. In astronautics control, the position coordinates are normally represented as the cartesian coordinate system and used a quaternions coordinate system for orientation representation because quaternions can represent the orientation of spacecraft better than physical angle and can overcome the problem of singularity. This paper aims to construct a model for both position and orientation estimation with public data. The input images are the dataset of Soyuz in the resolution of 1280x960, which is simulated by Unreal Engine 4. The implementation of this paper use GoogLeNet for a convolutional neural network model with the mathematical model of loss subject to direct regression. The result shows that a position estimation is significantly accurate with having distance error smaller than 1 meter and trand to reduce when setting a proper scaling factor for loss function. The result demonstrates a high error for orientation estimation. However, the experiment expresses that both position and orientation estimation can be improved in case of selecting a suitable scaling factor of loss function.
Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed.
Traveling Wave Fault Location for Lines Combined with Overhead-Lines and Cables Based on Empirical Wavelet Transform

D-type fault location method commonly used for hybrid line fault location is susceptible to the problem of asynchronous time difference of detection points, this paper proposes a single terminal fault location method for hybrid lines. Firstly, the segmentation criterion is constructed by using the feature of zero mode and line mode wave velocity difference, and then the fault location is completed by the single terminal method of zero mode and line mode wave speed difference. EMD can realize adaptive decomposition of traveling wave signals, but there is a problem of mode aliasing. Aiming at this problem, this paper proposes a traveling wave detection method based on empirical wavelet transform, which decomposes the fault traveling wave signal by EWT, and then uses the high frequency EMF2 component to accurately calibrate the traveling wave head. A large number of simulation results show that the fault location results of the proposed method are accurate and reliable.
A Broadband 25 to 42 GHz SiGe Upconverter

A 0.13 SiGe upconverter circuit is presented that is suitable for use in broadband millimetre-wave transmitters and active phased arrays. The circuit accepts four differential IF inputs (I+, I−, Q+, Q−) in the frequency range of DC to 13 GHz. An off-chip X-band LO passes through an on-chip tripler before being used to drive the image rejection mixer. The resultant output may be placed anywhere in the frequency range of 25 to 42 GHz. When all four inputs are utilised the RF signals are combined passively at the mixer outputs to achieve an output referred P1dB compression point of +4.4 dBm without the need for additional amplifiers in the RF signal path. This paper highlights the measured bandwidth and single-tone compression characteristic of the circuit. Layout techniques are also briefly described.
Improved Procedures for Training Primal Wasserstein GANs

Primal Wasserstein GANs are a variant of Generative Adversarial Networks (i.e., GANs), which optimize the primal form of empirical Wasserstein distance directly. However, the high computational complexity and training instability are the main challenges of this framework. Accordingly, to address these problems, we propose several procedures for improving the training of Primal Wasserstein GANs. We test the effectiveness of our proposed procedures on MNIST, CIFAR-10, LSUN-Bedroom and ImageNet-Dog category datasets, and the extensive experimental results confirm that our method is capable of generating high-quality images and obtaining high inception score. Importantly, we demonstrate that our method is more time efficient compared with other generative model techniques.
Dynamic Allocation for Complex Mobile Crowdsourcing Task with Internal Dependencies

Recently, mobile crowdsourcing utilizes the mobility of crowds and the computing capability of smart computing devices, which has received considerable attention from the research community. Recent pieces of literature focus on the multi-task allocation for mobile crowdsourcing, considering improving the accuracy of the collected data by inferring and utilizing the reliability of users. However, they neglect the fact that the internal dependency relationship exists on sub-tasks, which is decomposed via a divide-and-conquer method from the source tasks. Neglecting this fact may cause a series of dilemmas including high latency, low quality, low execution rate, and even more allocation errors. To address these problems, we should renormalize the task allocation problem of mobile crowdsourcing, and the task is divided into logically dependent sub-tasks, and then sub-tasks are sorted by the Depth-first search (DFS) algorithm. Different from previous approaches, an allocation method SGTA considering task internal dependencies is introduced to ensure that the workload does not exceed the executive ability of each worker and try to avoid misallocation. Based on the submission report during task execution, we devise a SimilAr Task Dynamic (SATD) allocation method to dynamically re-allocate these failure tasks. Finally, experimental results based on both synthetic and real data sets demonstrate that the proposed method is effective.
Drain Current Model of Reconfigurable Ferroelectric Field Effect Transistor (R-Fe-FET)

In this paper, drain current model of Reconfigurable Ferroelectric Field Effect Transistor (R-Fe-FET) is derived by solving current - continuity equation in cylindrical coordinates and using Landau - Khalatnikov equation. The model assumes gradual - channel approximation and by modulating the drain bias, the proposed device can operate either in an n-FET mode or in p-FET mode. Further, due to the presence of ferroelectric layer, the device offers superior electrical characteristics including I-V, transconductance and drain conductance over the conventional Reconfigurable Field Effect Transistor (R-FET). Thus, the proposed device finds extensive use in low-power analog/digital circuit design applications.
Circular Polarization Leaky-wave Antenna Based on Substrate Integrated Waveguide with Wide Axial Ratio Bandwidth for Millimeter-Wave Applications

A wide axial ratio bandwidth circular polarization (CP) leaky wave antenna (LWA) based on substrate integrated waveguide (SIW) is proposed in this paper for millimeter-wave (MMW) applications. Each element is composed of a slot on top side of the SIW, and two pairs of dipoles placed orthogonally. The slots couple out energy from the SIW and excite the cross dipoles through two vertical vias. Phase delay lines are used to connect the two pair of dipoles to produce the required 90 degrees phase difference for circular polarization. The proposed CP-LWA can achieve a broad impedance matching bandwidth about 29% (from 24.2 GHz to 32.4 GHz) with a peak gain of 17 dBi. The axial ratio (AR) is 0.9 dB at the main beam direction at 28 GHz. The antenna can keep a wide 3-dB axial ratio (AR) bandwidth about 13.79% when frequency changes from 27 GHz to 31 GHz.
Towards Automated Positioning of Ultrasonic Probes

This paper focuses on techniques to acquire good-quality ultrasound images that can be analyzed to provide an appropriate therapeutic regimen. We consider the problem of image-guided neuromodulation of the vagus nerve (VN). A robust template-matching method locates the VN from B-mode ultrasound images, with the nearby carotid artery (CA) being used as a biomarker. This information is used to correctly orient and position the probe.
Fake news detection using discourse segment structure analysis

Online news platforms greatly influence our society and culture in both positive and negative ways. As online media becomes more dependent for source of information, a lot of fake news is posted online, that widespread with people following it without any prior or complete information of event authenticity. Such misinformation has the potential to manipulate public opinions. The exponential growth of fake news propagation have become a great threat to public for news trustworthiness. It has become a compelling issue for which discovering, examining and dealing with fake news has increased in demand. However, with the limited availability of literature on the issue of uncovering fake news, a number of potential methodologies and techniques remains unexplored. The primary aim of this paper is to review existing methodologies, to propose and implement a method for automated deception detection. The proposed methodology uses deep learning in discourse-level structure analysis to formulate the structure that differentiates fake and real news. The baseline model achieved 74% accuracy.
Multiple GPU Parallel Strategy of Beam Tracing for Propagation Prediction in Large-Scale Scenes

Beam tracing (BT) can be used to simulate electromagnetic environment of urban area for its high efficiency and acceptable accuracy. As is known to all, implementing beam tracing on GPU will increase efficiency. However, when the scene scale is very large, one GPU is not sufficient to calculate all sites, besides, signal coverage is also a problem for conventional BT method. This paper provides a strategy of BT on multiple GPUs using OpenMP, and implements an optimized diffraction as well as multiple transmission on GPU to promote the coverage of our model. Numerical results demonstrate the high efficiency and good accuracy of proposed radio propagation model.
Docetaxel-Loaded Mixed Micelles and Polymersomes Composed of Poly (caprolactone)-Poly (ethylene glycol) (PEG-PCL) and Poly (lactic acid)-Poly (ethylene glycol) (PEG-PLA): Preparation and In-vitro Characterization

Microwave irradiation was used to synthesize poly (caprolactone)-poly (ethylene glycol) (PEG-PCL) and poly (lactic acid)-poly (ethylene glycol) (PEG-PLA) copolymers that are composed of biodegradable polymers including PEG, PLA, and PCL. These copolymers were used for loading docetaxel in nanoparticles. Single emulsion-solvent evaporation technique was applied for preparing the PEG-PLA and PEG-PCL mixed nanoparticles (micelles and polymersomes) with different proportions, including 0:1, 1:1, 3:1, 1:3, and 1:0. The unimodal gel permeation chromatography curve showed low polydispersity of the di-block copolymers. The in-vitro drug release curves of formulations were compared. Micelles and polymersomes of 75% PEG-PCL and 25% PEG-PLA (P5 and M5) have the lowest burst release (5%) at the same period compared to the other copolymers. The dynamic light scattering and TEM results clarified that the size and shape of the formulations are uniform. The cytotoxicity effect of P5 and M5 was evaluated in different cell lines. The best one was found to P5 with half maximal inhibitory concentration (IC50) between 1.48-11.79 µg/mL. The pro-apoptotic effect of P5 was confirmed with flow cytometry study. These mixed micelles (M5) and polymersomes (P5) was found to be superior formulations than non-mixed ones.
Malicious Code Detection Scheme Based on CWRNN for Microgrid Controllers

With the increase of power terminal devices and IoT devices which are accessed to the power grid, their security issues are becoming more and more important. While the microgrid controllers are served as the central core of the entire microgrid, the attacks on microgrid controllers and other power end devices have been launched in recent years. It’s obvious that the current safety protection measures for power end devices are insufficient. However, microgrid controllers cannot be protected by traditional intrusion detection systems or anti-virus software. Motivated by these concerns, this paper proposes a non-intrusive malicious code security monitoring scheme based on a power side channel. The core idea is to measure the power consumption data of the microgrid controller, to extract the power consumption feature, and to identify the abnormality sample through CWRNN neural network to determine whether the microgrid controller is attacked or not. The advantage of this method is that it can effectively detect unknown attacks without modifying the original software system. What’s more, the method is evaluated in the experimental test, and the detection accuracy can reach to 92%.
A Comprehensive Operation and Revenue Analysis Algorithm for LTE/5G Wireless System Based on Telecom Operator Data

The operation condition of wireless system directly impacts both the system performance and the quality of experience (QoE). On the basis of telecom operator data, this paper designs a comprehensive operation and revenue analysis (CORA) algorithm for LTE/5G wireless system. The objective of the proposed CORA algorithm is to analyze and evaluate both the operation state and the revenue of base station (BS) in the wireless system. The designed CORA algorithm is applied in the realistic 4G wireless system. The CORA algorithm can assist telecom operator to effectively analyze and evaluate the BS comprehensive state of operation and revenue.
Synthesizing Data for Collusion-based Malpractice of Shell Companies in Money Laundering

Money laundering is the process of hiding true source of illegitimate money and converting it into legitimate income. Shell companies are dummy companies masquerading as real companies, used extensively in money laundering. They work in a group referred as shell company set or simply shell set. A shell set is a typical collusion-based modus operandi. There is lack of availability of public domain datasets that can be used to detect shell sets. In this paper, we propose a novel approach of generating data synthetically which encompasses concept of collusion. We introduce this concept in the context of business operations of the companies to obtain group transactions. We enhance an existing Banking Transaction Simulator to generate group banking transactions of companies. The simulator generates dataset suitable to work with problem of shell set detection. We have demonstrated how the generated dataset is unbiased and close to real life data.
A Start-up Inrush Current Suppression Method Based on Dual Parameters Soft-start for PWM Rectifier

At the start-up moment, three-phase PWM rectifier generates a large inrush current on the AC side generally, which may cause electromagnetic interference and overcurrent protection, even damage to the switching device. In order to suppress the inrush current at start-up moment, the reference soft-start methods based DC Voltage and AC current are proposed respectively. However, suppression effects of such method are imperfect, and existing methods can hardly suppress the inrush current in the first few switching cycles after the rectifier is started. To solve the problem, the generation mechanism of inrush current is analyzed deeply in this paper from the perspective of switching mode and modulation ratio of the PWM rectifier, and mathematical model of three-phase PWM rectifier on each switching mode is derived. The impact of initial modulation ratio on amplitude of inrush current is obtained. Therefore, in this paper, a start-up inrush current suppression method based dual parameters soft-start, which are modulation ratio and DC voltage reference respectively, is proposed. Finally, the simulation and experimental results show that the proposed method can effectively eliminate the inrush current during the whole start-up procedure of the PWM rectifier.
3D Convolutional Adversarial Micro-networks for Low Count PET-MR Post-processing

In positron emission tomography (PET), decreasing patient injected radiation dose or scan time leads to a reduction in acquisition counts and noisy reconstructed images. Approaches such as post-smoothing (PS) and total variation (TV) denoising can help reduce noise at the cost of resolution or bias, respectively. However, recent advances in convolutional neural networks (CNNs) have lead to promising results in low count PET. Some of these can take advantage of jointly acquired data from other modalities such as magnetic resonance (MR) imaging. However, there are many considerations when designing an architecture depending on the specific task and data available. This work compares some of the current state-of-the-art approaches. Results demonstrate that more powerful, complex networks do not necessarily yield better performance. Normalised root mean squared error (NRMSE) is decreased by 85.5% compared to standard maximum likelihood expectation maximisation (MLEM) PET reconstruction by using a 3-layer micro-network on very low (3.01M) count simulation data. This compares to reductions of 80.2% and 85.3% when total variation (TV) edge enhancement and an adversarial discriminator are incorporated into the loss function, respectively. Using both TV and a discriminator term produces a slightly better whole-brain NRMSE reduction of 86.0%, though with a risk of false positive lesions. This indicates that more investigation is required to determine general rules for choosing architectures and hyperparameters for low count PET.
Status Detection System for Power Plugs Based on Data Sampling

Power supply system is the basis for the healthy operation of microcomputer protection. This paper presents a new method to detect the health status of power plugs and design corresponding detection device, including data acquisition, data processing and data analysis. Using pointer ammeter, pointer voltmeter, disc high-power disk adjustable resistance, switching switch, micro oscilloscope and epoxy resin board, the detection device was built to measure the output voltage, current, ripple and start-up time of power plug with different voltage levels under different load current conditions. According to raw data, the ‘voltage/power curves’ and ‘start voltage timing diagrams’ are drawn. Compared with ‘standard curves’ acquired from test results of a large number for new power plugs in the same type, the health status of power plugs can be determined.
Autonomous System Design for Dam Surveillance Robots

Dams are important engineering facilities in water conservancy industry. They have many functions, such as flood control, electric power generation, irrigation, water supply, shipping, etc. Therefore, their own long-term safety is crucial to operation stability. Because of the complexity of the dam environment, robots with kinds of sensors are a good choice to replace humans to perform surveillance job. In this paper, an autonomous system design is proposed for dam surveillance robots, which includes general solution, electromechanical layout, sensors scheme and navigation method. By connecting the automatic robot, the cloud server and the users with IOT technology, the proposed solution could be more robust and practical.