Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy and Repeatability Study of An Elbow Exoskeleton for Multistage Exercises

The need for post-stroke rehabilitation is paramount especially with the increasing number of stroke survivors worldwide. It appears that patients must receive multistage therapy consisting of specific exercises to improve their neuro-motor function. Modern-day advancement in biomedical technology has transformed the exoskeleton into a potential tool for post-stroke rehabilitation. Several exoskeletons have been developed for providing post-stroke exercises, however, their actions are influenced by several inhibiting factors such as abnormal sensor data, continuous usage of power source, safety from anatomical range etc. Besides their working principle is limited to providing a specific type of exercise. To overcome the current limitations of the existing systems for providing multistage exercises in post-stroke rehabilitation, a novel elbow exoskeleton has been developed where joint movements are supported by an electric motor or springs in different stages. The innovative mechanism incorporates the benefits of an electric motor and springs; the electric motor is used to maintain external force-controlled joint movement during the acute stage whereas joint motion is partially assisted or resisted by spring force in the recovery stage hence minimizing the energy consumption from motors. A pilot experiment has been conducted among healthy participants to measure the joint parameters and to analyze the accuracy and repeatability of the measurements by the exoskeleton. The measured joint parameters from the exoskeleton have been also compared with the Kinect motion analysis system where the measurement error in joint angle is around 1% to 6%. The correlation coefficient between consecutive joint measurements is higher than 0.99 proving the high repeatability of measurement.
A Reconfigurable Submillimeter-Wave Polarizer Based on Phase-Change Materials

A reconfigurable sub millimeter-wave polarizer based on phase-change materials is presented in the work. The polarizer consists of7×7 microstrip patch units. The patch in the unit is excited by the slot on the bottom layer. Four corners of the patch are coated with GeTe phase-change materials. By changing the state of the GeTe through laser, the polarizer can convert the linear polarization to left-hand and right-hand circular polarization. The polarizer has an impedance bandwidth of 6.4% from 96 to 102.4 GHz and an axial-ratio bandwidth of 8.4% from 96 to 104.4 GHz. The proposed polarizer has potential applications in imaging, tomography, sensing, and 6G communication.
Classifying Insect Pests from Image Data using Deep Learning

The fact that insecticidal pests impair significant agricultural productivity has become one of the main challenges in agriculture. Several prerequisites, however, exist for a high-performance automated system capable of detecting nuisance insects from massive amounts of visual data. We employed deep learning approaches to correctly identify insect species from large volumes of data in this study model and explainable AI to decide which part of the photos is used to categorize the insects from the data. We chose to deal with the large-scale IP102 dataset since we worked with a large dataset. There are almost 75,000 pictures in this collection, divided into 102 categories. We ran state-of-the-art tests on the unique IP102 data set to evaluate our proposed solution. We used five different Deep Neural Networks (DNN) models for image classification: VGG19, ResNet50, EfficientNetB5, DenseNet121, InceptionV3, and implemented the LIME-based XAI (Explainable Artificial Intelligence) framework. DenseNet121 outperformed all other networks, and we also implemented it to classify specific crop insect species. The classification accuracy ranged from 46.31 percent to 95.36 percent for eight crops. Moreover, we have compared our prediction to that of earlier articles to assess the efficacy of our research.
Improving Speech Emotion Recognition via Fine-tuning ASR with Speaker Information

Speech emotion recognition (SER), which derives the emotion labels (happiness, anger, sadness, and neutral state) from speech inputs, is an important component of human-computer interaction (HCI) interfaces. However, the following issues limit the capability of SER. First, small scale of currently existing datasets is insufficient for training and reliably evaluating more advanced neural models. Second, it can be observed that SER performance is affected by linguistics, phonetics, and speaker information. However, previous works only focused on one of them or largely omitted speaker information from SER systems while human emotion is highly individualistic. To address these issues, we propose to fine-tune an automatic speech recognition (ASR) model based on Conformer, a recent advanced deep learning architecture previously trained on large-scale datasets, for the downstream task of SER. This helps our SER model utilize numerous available ASR datasets to well capture phonetics and linguistics in each speech utterance. In addition, we propose to perform segment-wise concatenation of speaker embedding and statically-pooled ASR embeddings to simultaneously encode both phonetics, linguistics and speaker information into a single speech segment representation. Experimental results demonstrated that our proposed model achieves state-of-the-art (SoTA) performance on the benchmark interactive emotional dyadic motion capture (IEMOCAP) corpus.
Effective Attribute Selection for Multi-dimensional Root Cause Analysis

Using large-scale multi-dimensional data for root cause analysis (MDRCA) is vitally important for online software services. It helps operators narrow down the scope of anomalies and failures quickly and localize the root cause to a finer granularity. However, most existing MDRCA algorithms can only solve low-dimensional problems. When dealing with high-dimensional data, the complexity of these algorithms would significantly increase, and even some algorithms would no longer work. Intuitively, passing only a subset of attributes rather than full attributes can improve the performance of these MDRCA algorithms. However, it is challenging due to data imbalance and novel root cause attributes. To better understand the problem of root-cause-oriented attribute selection (RCOAS), we conduct a preliminary study based on real-world data. We find that there exist several straightforward rules to filter out some attributes. In addition, we reveal that existing approaches do not fit the requirements of RCOAS. Motivated by the study, we propose an RCOAS approach, RC-LIR, to select a subset of attributes for downstream algorithms. RC-LIR first performs rule-based selection. Then it improves a feature selection algorithm by two strategies, i.e., scaling up imbalanced data and considering the redundant cost. Experiments on 1000 real-world fault cases demonstrate that RC-LIR can achieve an F1-score of 0.88, outper-forming the baseline approaches by at least 0.15. Furthermore, our experiments with four widely adopted MDRCA algorithms show that integrating RC-LIR can lead to more effective and efficient MDRCA.
Using Self-learning Representations for Objective Assessment of Patient Voice in Dysphonia

The assessment of patients' voice quality with vocal system impairment is crucial in the clinical setting. Standard assessment involves asking the patient to pronounce continuous vowels, followed by quantified Grade, Roughness, Breathiness, Asthenia, and Strain (G RBAS) scores rated by voice specialists. Nevertheless, vowels contain limited information, and it is also time-consuming and complicated to make subjective assessments for increasing the precision. Therefore, in this paper, we use self-supervised learning technique and neural network to auto-matically assess the Grade of speech samples and vowel /a/ of patients, whose vocal systems are impaired to varying degrees. Experimental results show that the proposed method can achieve the best mean squared error (MSE) of 0.201 and 0.141 in utterance level and patient level on speech data, which indicates reliable predictions can be provided considering the range of GRBAS scales.
Reinforcement Learning applied to the Routing and Spectrum Assignment in Elastic Optical Networks

Elastic Optical Networks (EON) has emerged as technology in optical networks whose architecture can respond to the growing need for elasticity in allocating optical network resources. EON imposes physical layer constraints on traffics. In this context, developing efficient Routing and Spectrum Assignment (RSA) algorithms for dynamic traffic is critical to EON’s success. Strategies based on Reinforcement Learning (RL) emerge as a valid alternative to dynamic RSA, due to its ability to adapt to changes in the state of the network by learning process. In this work, we propose RL algorithms based on Q-learning to solve the dynamic RSA problem. First, we study the performance of routing algorithms that evaluate the learning Q values for a set of pre-calculated disjoint routes by selecting a route that corresponds to a minimum blocking rate. Similarly, we studied Q-learning algorithms for spectrum assignment, which selects a block of the spectrum from a pre-routed path to minimize the blocking rate. Numerical simulations on different dynamic traffic loads show that the performance of the Q-learning-based proposals is promising in obtaining a better blocking rate than the state-of-the-art heuristic approaches.
Perfect Bit Fields and Their Technical Application

By a Perfect Bit Field (PBF) we understand a 2-dimensional binary array in which each possible binary subarray of fixed dimensions appears exactly once. The topic of PBFs is closely connected to the theme of de Bruijn sequences which are 1-dimensional analogies of PBFs. Unlike de Bruijn sequences the construction of non-trivial PBFs is relatively difficult and technically rather demanding. The topic of PBFs appears from time to time but there is no systematic approach and overall theory available. We provide a short survey of available techniques and present our own results aimed at the practice of PBFs generation. We also present a possible industry application of PBFs. The application consists in the exact position location of a soil excavator bucket by means of a camera system. The aim is to provide feedback between the excavator bucket target and real position during the excavator tests with the loaded bucket.
VocEmb4SVS: Improving Singing Voice Separation with Vocal Embeddings

Deep learning-based methods have shown promising performance on singing voice separation (SVS). Recently, embeddings related to lyrics and voice activities have been proven effective to improve the performance of SVS tasks. However, embeddings related to singers have never been studied before. In this paper, we propose VocEmb4SVS, an SVS framework to utilize vocal embeddings of the singer as auxiliary knowledge for SVS conditioning. First, a pre-trained separation network is employed to obtain pre-separated vocals from the mixed music signals. Second, a vocal encoder is trained to extract vocal embeddings from the pre-separated vocals. Finally, the vocal embeddings are integrated into the separation network to improve SVS performance. Experimental results show that our proposed method achieves state-of-the-art performance on the MUSDB18 dataset with an SDR of 9.56 dB on vocals.
Nonlinear Residual Echo Suppression Based on Gated Dual Signal Transformation LSTM Network

Although adaptive filters play a vital role in the acoustic echo cancellation system, multiple factors prevent them from completely eliminating the echo signal. Consequently, additional suppression module is required and crucial for enhancing the echo cancellation performance. In this work, we propose a gated dual signal transformation LSTM network (Gated DTLN) that improves upon the recently developed Dual Signal Trans-formation LSTM Network for AEC (DTLN-aec). The gated convolution units are inserted to enhance filtering features in the time domain part of the model, while the echo reference signal is removed from the input of this part to reduce the complexity of the mask generator. The experimental results on different signal-to-echo ratio (SER) datasets demonstrate the superiority of our proposed method.
Fast Integral Terminal Sliding Mode Control of PMSM Based on New Sliding Mode Reaching Law

For the purpose of improving the dynamic quality of Permanent Magnet Synchronous Motor (PMSM) control system, a control method combining Fast Integral Terminal Sliding Mode Controller (FITSMC) and Sliding Mode Disturbance Observer (SMDO) is proposed. Firstly, to make the speed error converge faster, a fast integral terminal sliding mode surface is proposed, which can overcome the problem that the integral sliding mode surface cannot converge in finite time and the singularity of terminal sliding mode surface. Secondly, a New Sliding Mode Reaching Law (NSMRL) is developed which can shorten the response time and improve the dynamic characteristics of PMSM control system. In addition, the SMDO is established to observe and compensate the load disturbance. Finally, Integral Sliding Mode Controller (ISMC) and FITSMC are applied to PMSM control system respectively. The simulation results show that FITSMC makes the robustness and dynamic response of the system stronger and faster.
DC Suitability of AC Appliances

DC appliances are increasingly becoming common-place with the advances in switch-mode power supplies and renewable energy systems. Current devices are designed to operate on 230 VAC. However, many of them include an AC/DC stage that incurs unnecessary losses. This paper investigates the power supply stages of several household appliances to determine their suitability to be supplied directly from a DC source. Three categories of home appliances were analysed: Switch Mode Power Supplies (SMPS), resistive heaters, and lighting equipment. Devices containing SMPS can operate on 325 VDC without any modification as most of their components are already rated for DC. Resistive heaters could work on 230 VDC with minor modifications as their contact switches are not rated for breaking DC currents. Appliances such as phase control circuits, direct-on-line induction machines and step-up/down transformers would need significant modification to operate on DC as these appliances rely on an AC supply to operate.
Intelligent Energy Efficient Resource Allocation for URLLC Services in IoV Networks

Internet of vehicles (IoV) has been developed as a promising technology to improve road safety. However, resource management can be challenging in a congested traffic environment, which can affect the energy efficiency (EE) and spectrum efficiency (SE) in IoV networks. In this paper, we present a novel intelligent resource allocation approach based on deep reinforcement learning to maximize the weighted composite efficiency that incorporates the EE and SE metric subject to latency and reliability constraints of vehicle-to-vehicle (V2V) users. We employ Thompson sampling with double deep Q network to transform the objective function. Moreover, we present a probability-based learning approach to meet the quality of service requirements and to increase the learning ability of the proposed model. The simulation results indicate that the proposed approach maximizes the composite efficiency while satisfying the latency and reliability constraints of V2V users.
Prediction-Uncertainty-Aware Decision-Making for Autonomous Vehicles

Motion prediction is the fundamental input for decision-making in autonomous vehicles. The current motion prediction solutions are designed with a strong reliance on black box predictions based on neural networks (NNs), which is unacceptable for safety-critical applications. Motion prediction with high uncertainty can cause conflicting decisions and even catastrophic results. To address this issue, an uncertainty estimation approach based on the deep ensemble technique is proposed for motion prediction in this paper. Subsequently, the estimated uncertainty is considered in the decision-making module to improve driving safety. Firstly, a motion prediction model based on long short-term memory (LSTM) is built and the deep ensemble technique is utilized to obtain both epistemic and aleatoric uncertainty of the prediction model. Besides, an uncertainty-aware potential field is developed to process the prediction uncertainty. Furthermore, a decision-making framework is proposed based on the model predictive control algorithm that considers the uncertainty-aware potential field, road boundaries, and multiple constraints of vehicle dynamics. Finally, the public available NGSIM, HighD and INTERACTION datasets are used to evaluate the proposed motion prediction model. More importantly, two traffic scenarios are also extracted from NGSIM and INTERACTION datasets to verify the effectiveness of the proposed decision-making method and in particular, its real-time performance is shown by employing a hardware-in-the-loop (HiL) experiment bench.
Forecasting Dengue Fever in France and Thailand using XGBoost

Dengue virus infection has been classified by The World Health Organization (WHO) as a major international public health concern because of many factors such as expanding geographic distribution, increased endemic frequency, the co-circulation of multiple virus serotypes, and new emerging region of dengue hemorrhagic fever. This mosquito-borne disease is transmitted by mosquitoes of genus Aedes, such as A. albopictus and Aedes aegypti. The emerging factors are complicated and include climate factors, degree of urbanization, quality of vector control, the evolution of virus, and international travel. This research aimed to propose the model for short-term and long-term forecasting dengue fever endemics in France and Thailand using Extreme Gradient Boost Model (XGBoost). France has been selected to represent as a European country which most infections were imported from Asia, while Thailand represents Southeast Asia country where dengue fever is endemic. In addition, we used surveillance data from various data sources, the world's daily meteorological data, and related keyword queries from Google Trends as input features. We applied cross-correlation analysis to identify the time lag between surveillance data and other input features. Moreover, we experimented with various combinations of training input parameters to optimize the accuracy of the prediction model.
On The Optimal Classifier For Affective Vocal Bursts And Stuttering Predictions Based On Pre-Trained Acoustic Embedding

Speech emotion recognition currently gained more interest from researchers due to its potential applications in the market. Instead of a speech, vocal bursts are understudied and may contain richer affective information than speech for recognizing emotion (e.g., laugh for happiness and cry for sadness). On the other side, acoustic features used for the affective vocalization may also be helpful for the stuttering evaluation task. Instead of handcrafted acoustic features, a pre-trained model feature extractor is now attaining more attention due to its competitiveness in modeling universal speech embedding. However, the previous speech embedding evaluations are not well-suited for emotion recognition. In this study, the researchers evaluated acoustic embedding extracted from a model fine-tuned on an affective speech dataset for affective vocalization and stuttering predictions using different classifiers. The methods were evaluated on a baseline classifier from the previous study and five new different classifiers, including an ensemble classifier. The results show improvements over the baseline methods; the ensemble classifier consistently resulted in the optimal performance on new validation sets with balanced and unnormalized data for both affective vocal bursts and stuttering predictions.
A Wideband E-plane Vertical Transition from Rectangle Waveguide to Groove Gap Waveguide

Gap waveguide (GW) technology solves the electrical contact problem through a contactless way and makes the packaging of multilayer millimeter wave systems much easier. In this work, a wideband E-plane vertical transition from hollow rectangle waveguide (RW) to groove gap waveguide (GGW) for multilayer millimeter-wave packaged system is proposed. An inverted bended stepped structure is designed for wideband impedance matching between the vertical RW and the horizontal GGW. An air gap exists between the bended steps and the cover of GGW to maintain the advantage of contactless. To avoid resonation between the bended steps and the top cover plate, the width of the bended steps is narrowed and a rectangular region is cut off to reduce the surface of the bended steps further. A Ka-band prototype of the transition is designed, fabricated and tested, over the entire Ka-band, the measured insertion loss is less than 0.15dB, and the return loss is better than 20dB.
PUTraceAD: Trace Anomaly Detection with Partial Labels based on GNN and PU Learning

Distributed tracing has been an important part of microservice infrastructure and learning-based trace analysis has been used to detect anomalies in microservice systems. Existing learning-based trace anomaly detection approaches ei-ther assume that trace patterns can be learned from normal execution or rely on fault injection to produce labeled traces (i.e., normal/anomalous ones). However, in practice it is often difficult to ensure that the normal execution does not involve anomalous traces or obtain a large variety of normal and anomalous traces through fault injection. In this paper, we propose PUTraceAD, a trace anomaly detection approach that can alleviate the above problems. PUTraceAD represents a trace as a span causal graph with node features such as operation name, response code, duration time. Based on the graph representation, PUTraceAD trains a GNN- and PU learning-based trace anomaly detection model. During the process, PU (Positive and Unlabeled) learning optimizes model parameters through estimating the data distribution. Therefore, PUTraceAD can train the model based on a small set of labeled anomalous traces and a large set of unlabeled traces. Our evaluation shows that PUTraceAD outperforms existing unsupervised trace anomaly detection approaches and only slightly underperforms a supervised learning-based approach that takes full advantage of labeled traces.
Thermal and Ventilation Analysis of PMSG with a Radial External Fan

This article focuses on the thermal model of a synchronous generator with permanent magnets and external ventilation. The model was made in the commercial software Ansys Fluent. The model is validated by measuring a real machine. For this purpose, three heating tests with different cooling parameters were measured of which is only one presented. Due to the sufficient temperature reserve, the machine's nominal flow, half flow, and self-ventilation were chosen. The aim of the work was to tune the model to a numerical model and a real machine.
Study on the effect of random spread spectrum on the vibration noise of underwater propulsion motor

This paper takes the deep-sea underwater propulsion motor as the research background and the permanent magnet synchronous motor(PMSM) as the research object. To meet the performance requirements of high control accuracy and low noise of underwater propulsion motors, this paper studies the harmonic voltage peak generated by Space Vector PWM(SVPWM) modulation. The suppression mechanism of harmonic peak distribution and random spread spectrum modulation strategy is analyzed. Based on random spread spectrum modulation, the double random spread spectrum algorithm with a better spread spectrum effect is studied. Finally, an improved scheme based on double random spreading algorithm is proposed. The simulation results show that the improved double random spread spectrum algorithm outperforms the traditional double random spread spectrum algorithm