Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech emotion recognition based on the reconstruction of acoustic and text features in latent space

Speech emotion recognition (SER) has been actively studied in the recent decade and has achieved promising results. Most state-of-the-art SER methods are based on a classification approach that ultimately outputs the softmax probability over different emotion classes. On the other hand, we have recently introduced an anomalous sound detection approach to improve the SER performance of the neutral class. It uses a neutral speech detector consisting of an autoencoder that reconstructs acoustic and text features in latent space and is trained using only neutral speech data. The experimental result confirmed that the reconstruction error could be successfully used as a cue to decide whether or not the class is neutral and suggested that it could be applied to other emotion classes. In this paper, we propose an SER method based on the reconstruction of acoustic and text features in latent space, in which the reconstructor for different emotion classes, including the neutral class, is used. The proposed method selects the emotion class with the lowest normalized reconstruction error as the SER result. Unlike the classifier approach, one reconstructor is dedicated to each emotion class and trained using only the data of the target emotion class. Therefore, the reconstructor can be trained without being affected by imbalanced training data and also facilitates the application of data augmentation to only a specific emotion class. Our experimental result obtained using the IEMOCAP dataset showed that the proposed method improved the class-average weighted accuracy by 1.7% to 77.8% compared with the state-of - the-art SER methods.
Structural characterization of carbon nanosheets via x-ray scattering

The structure of carbon nanosheets deposited by radio frequency plasma-enhanced chemical-vapor deposition at different substrate temperatures is investigated via x-ray scattering. Carbon nanosheets consist of vertically aligned graphene-layer stacks, one to nine layers thick, which can attain micron-scale lengths. Histograms of both molecule length and thickness are generated by fitting the experimental data with a linear combination of x-ray scattering intensities, which are calculated for rhombus-shaped molecules of different dimensions. These histograms show that the average uncorrugated length within a nanosheet decreases from 107A at a 670°C deposition temperature to 50A at 950°C. The distribution of nanosheet thickness remains qualitatively similar at each deposition temperature, but decreases from an average of eight graphene layers at 670°C to about six layers at 950°C. With increasing temperature large nestlike structures are observed, but are found to consist of the same nanosheet constituents i.
TASEP model on a crossing of microtubules

The totally asymmetric simple exclusion process (TASEP) on a crossing of microtubules has been studied. This 3-dimentional structure has been unwrapped into an ensemble of 1-dimensional or quasi-1-dimensional trajectories. We used mean field method to give the compound densities and currents. On this basis, the phase diagram of the compounds can be obtained. We structured such ‘sweeps’ in the entrance rate α, for fixed exit rate β and got the discontinuous densities and continuous currents.
An AI Based Approach for Personalized Nutrition and Food Menu Planning

Dietary intake is considered one of the major research issues in the field of nutrition and health care. However, existing tools are time consuming and require skilled people to interview patients and collect useful data. In this paper, we start by determining the nutritional needs of patients and then we propose a method for personalizing meals using artificial intelligence (AI) methods. The basic idea is to use images of food before and after consumption and to estimate nutrient intakes using image analysis methods. These methods are rapidly evolving with applicable solutions in the nutrition field towards the use of machine learning algorithms. Finally, the design and architecture of our prototype are detailed and evaluated in order to enhance the system functionalities.
Region Adaptive Self-Attention for an Accurate Facial Emotion Recognition

Recognizing facial emotion is important in human communication, especially non-verbal communication. Despite the recent advancements in deep learning, facial emotion recognition has not achieved high performance compared to other classification tasks. Motivated by the mechanism of human visual perception, in which humans recognize the facial emotion by combining the informative facial regions (i.e., eyebrows, eyes, nose, and mouth) with different weights, we propose a novel facial emotion recognition network. To effectively train the informative facial regions, we introduce adaptive patch extraction and region adaptive self-attention schemes. The adaptive patch extraction initially decides the informative facial region based on the human facial perception. Then, based on the decided informative facial regions, attention weights between regions are estimated from the region adaptive self-attention scheme. Finally, by combining the features of facial regions with attention weights, the proposed network accurately recognizes facial emotion. The experimental results show that the proposed network effectively focuses on the informative region of the human face. Furthermore, through the comparison of facial emotion recognition accuracy, it is verified that the proposed network remarkably outperforms the state-of-the-art methods.
Multi-loss Function in Robust Convolutional Autoencoder for Reconstruction Low-quality Fingerprint Image

Our research is fingerprint reconstruction based on a convolutional autoencoder. We combine the perceptual measurement as a multi-loss function to give satisfactory weight correction, such as the structural similarity index measure (SSIM), Mean Absolute Error (MAE), and peak signal-to-noise ratio (PSNR). We observed and investigated the result using multi-loss functions and other loss functions. Eventually, our experiment obtained the highest image quality metric scores from the experimental result summarized as a loss function (SSIM + PSNR) with optimizer Root Mean Squared Propagation (RMSprop). We evaluated the image reconstruction using a dataset from FVC2004. Eventually, our proposed method gets impressive results, increasing the image's average quality by PSNR of 20.58%, SSIM of 4.07%, and MSE of 38.92%, respectively.
Eigenmode Analysis of DC-to-THz Transmission Line Based on the Finite-Difference Frequency-Domain Method

To meet the demand for low-dispersion and broad-band transmission on DC-to-THz band, the mode-selective transmission line (MSTL) was proposed. In this paper, a two-dimensional (2-D) finite-difference frequency-domain (FDFD) method is proposed for eigenmode analysis of the MSTL. Two cases of the MSTL with different dielectric materials and their corresponding modes are analyzed. When the dielectric constant inside of the MSTL is different from the dielectric constant out-side, the dominant mode is a frequency-dependent mode, which looks like transvers electromagnetic (TEM) mode at low-frequency band and TE10mode at high-frequency band. On the contrary, when the dielectrics inside and outside are the same the dominant mode is a pure TEM mode.
Optimal Design of Power Transformer Magnetic Shielding Utilizing Extreme Learning Machine and Particle Swarm Optimization

Aiming at the problem of excessive leakage magnetic field and stray loss of power transformers, the three-dimensional nonlinear eddy current field finite element calculation method is applied to calculate and analyze the leakage magnetic field and stray loss of a large power transformer. In order to overcome the problem that the efficiency of the transformer is affected by the excessive leakage magnetic field and stray loss in the structural parts, shielding measures are taken for the transformer according to the calculation results. The shielding measures for the leakage magnetic field and stray loss of transformer structural parts effect of loss are carried out. After determining the shielding structure, the extreme learning machine combined with particle swarm optimization method is utilized to optimize the shielding structure for better shielding effect.
Evaluation of Switching Ripple Effect on Efficiency of Novel Spoke-Type IPMSM Using Dy-Free Magnet-Comparison to IPMSM using NbFeB Magnet

This research aims to develop a low-cost, high-efficiency traction motor. A novel spoke-type interior permanent magnet synchronous motor (IPMSM) is proposed and evaluated in detail. Although the proposed IPMSM uses a Dy-free magnet and a ferrite magnet, the same torque can be obtained as the IPMSM mounted on TOYOTA Prius 4th generation. Therefore, the proposed IPMSM contributes to reducing the magnet cost. In this paper, the proposed IPMSM was evaluated in detail considering the switching ripple caused by the inverter and compared with Prius’s IPMSM. As a result of simulation, the proposed IPMSM has higher efficiency than that of the Prius’s IPMSM at frequently used operating points under realistic low switching frequency.
A predictive approach for dynamic replication of operators in distributed stream processing systems

Stream Processing Systems (SPSs) can present significant fluctuation in input rate. To address this issue, some existing solutions propose reconfiguring the SPS by replicating its operators. However, such reconfiguration usually induces a high system downtime cost. Moreover, reconfiguration decisions are based only on resource utilization without balancing the load between replicas. We propose in this paper a predictive SPS that dynamically defines the necessary number of replicas of each operator based not only on the current resource utilization and input rate variation but also on the events that, due to the operator's overloading, could not be processed yet and are, thus, kept in the operator's queue. In addition, our SPS implements a load balancer that distributes incoming events more evenly among replicas of an operator. Our solution has been integrated into Storm. To avoid system reconfiguration downtime, our SPS preallocates a pool of replicas where each of them can be activated or deactivated based on per operator input load predictions. Using real traffic traces with different applications, we have conducted experiments on Google Cloud Platform (GCP), evaluating our SPS and comparing it with Storm and DABS-Storm.
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts Tasks

Understanding humans' emotions is a challenge for computers. Nowadays, research on speech emotion recognition has been conducted progressively. Instead of a speech, affective information may lay on short vocal bursts (i.e., cry when sad). In this study, we evaluated a recent self-supervised learning model to extract acoustic embedding for affective vocal bursts tasks. There are four tasks investigated on both regression and classification problems. Using similar architectures, we found the effectiveness of using a pre-trained model over the baseline methods. The study is further expanded to evaluate the different number of seeds, patiences, and batch sizes on the performance of the four tasks.
Modelling dynamos in rapidly rotating late-type stars

There is growing observational evidence that magnetic activity in rapidly rotating late-type stars is very different from that found in the Sun. In particular, polar spots and other high-latitude features are often observed on rapid rotators. In this Letter, I propose a plausible rotation profile for these rapidly rotating stars and this is then built into a non-linear mean-field dynamo model. It is shown that this model naturally gives rise to polar magnetic features and the relevance of these results to rapidly rotating late-type stars is discussed.
Divide and Conquer: A Low-complexity Neural Network for Monophonic Speech Enhancement

Noise suppression is an essential speech enhancement method to reduce the background noise in communication systems. Although stationary-noise can be successfully suppressed with the conventional signal processing algorithm, the non-stationary noise, especially the instantaneous-noise, remains challenging. Impressive performance has been achieved after neural network introduced in noise suppression task in the past decade. However, good performance and low complexity is still alternative. In this study, we demonstrate a low-complexity neural network for monophonic speech enhancement, in which a divide and conquer strategy has been employed in the design of the neural network to separate the noise suppression task into an envelope enhancement module and a detail enhancement module, denoted as EDNet. This approach achieves significant quality improvements in terms of the perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STDI), and the Scale-Invariant Signal to Distortion Ratio (SI-SDR) with low computational complexity.
Experimental study of piezoelectric microfluidic hybrid pump with double-inlet and double-chamber

In order to investigate the factors affecting the fluid mixing ratio in the piezoelectric hybrid pump, so as to make a reference for designing the piezoelectric hybrid pump that can accurately adjust the fluid mixing ratio, this paper analyzes the factors affecting the fluid mixing ratio of the piezoelectric pump and proposes a piezoelectric microfluidic hybrid pump with double-inlet and double-chamber. On the basis of analyzing the working principle of liquid transfer and liquid mixing of this piezoelectric pump, the effect of the different heights of the pump chamber on both sides of the piezoelectric pump on the input liquid flow rate on both sides and the effect of the different depth of the seal groove at the piezoelectric vibrator on the input liquid flow rate on both sides are experimentally investigated. The experiments show that the different depths of the seal grooves on both sides of the double-inlet double-chamber piezoelectric pump will cause changes in the inlet suction flow on both sides, and in the range of 30 to 110Hz, the flow rate difference between the two inlets can be up to 10ml/min; there exists a pump chamber with a better performance of 1mm in height, one side with a pump chamber height of The results show that the different seal groove depth and pump chamber depth on both sides of the piezoelectric pump will make the suction flow on both sides different, which has good reference value for the study of adjusting the mixing ratio.
An Optimal Vehicle Counting Framework for Non-CCTV Placements

Automated video surveillance requires CCTV to be strategically placed. However, most available CCTVs are not placed in the most optimal setting for object detection and counting. Most detection and counting algorithm focus on a canonical and vertical view of the road which is easily installed in highways and enables easy counting. However, these methods perform poorly in non-canonical or horizontal high angled CCTVs. Although they provide better coverage, objects are smaller in size with different scales in both traffic lanes. Therefore, this study proposes an optimal counting framework that incorporates guided tiling to help detect smaller objects in horizontal views. An object detection/tracking model is also selected and trained. At the same time, we conduct an ablation study to study the effects of tiling and different counting techniques. Experimental results show that our counting framework can achieve up to 23.2% counting improvement for non-canonical scenes.
Highly nonlinear contact interaction and dynamic energy dissipation by forest of carbon nanotubes

Mechanical response and energy dissipation of an array of carbon nanotubes under high-strain rate deformation was studied using a simple drop-ball test with the measurement of the dynamic force between the ball and forest of nanotubes. This convenient process allows extracting force–displacement curves and evaluating dissipated energy by the nanotubes. The contact force exhibits a strongly nonlinear dependence on displacement being fundamentally different than the Hertz law. The forest of vertically aligned nanotubes may be used as a strongly nonlinear spring in discrete systems for monitoring signal propagation speed, and as a microstructure for localized energy absorption.
Research on Influencing Factors of Air Friction Loss of High-speed Magnetic Suspension Motor

The air friction loss on the rotor surface of high-speed magnetic suspension motor is quite large in value. But it is difficult to be determined theoretically. Based on the fluid field analysis, this paper explores the influencing factors of air friction loss on the rotor surface of high-speed magnetic suspension motor, including rotating speed, outer radius of rotor, axial airflow velocity, air gap length, axial length, and roughness height of rotor surface. In addition, the air friction loss can be separated by measuring the total losses. Finally, the accuracy of the analysis is verified by the air friction loss experiment on a high-speed magnetic suspension motor.
H-Plane Radiation Pattern Decoupled Patch Antennas with Zero Edge-to-Edge Spacing Using Three-Pair Vias

An $H$-plane radiation pattern decoupled patch antennas with zero edge-to-edge spacing is presented in this paper. When the two patches are placed together, a higher-order TM11 mode can be excited, and the mutual coupling between the two patch antennas is high with tilted radiation patterns. Therefore, three pairs of metallic vias are inserted into the interface to obtain a radiation pattern decoupling effect, namely a high isolation and boresight radiations simultaneously. To verify the idea, a prototype with zero edge-to-edge spacing is designed at 2.4-GHz WLAN band. Simulation results show that the proposed patch antennas have an isolation larger than 15 dB, whereas the radiation patterns also show symmetry and retain maximum radiation direction along z-axis.
Positive and Negative Influence Factors in Users' Willingness of Mobile Payment in Taiwan

This study intends to explore the relationship between positive and negative influence factors in users' willingness of mobile payment and their use intention and behavior. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study increases two variables including perceived risk and mental accounting. In this study, 455 questionnaires were distributed to users using mobile payment in Taiwan, 427 of which were recovered, with a recovery rate of 93.8% and 413 were valid, with an effective recovery rate of 90.8%. The conclusion is as follows: social influence, facilitating conditions, and habit have a positive and significant impact on the behavioral intention of mobile payment; mental accounting has negative and significant impact on behavioral intention. Facilitating conditions, habit and behavioral intention have positive and significant impact on use behavior, i.e. impact on users' cognitive decision making.
A 28-GHz Series-Parallel Combined Doherty Power Amplifier with PBO Efficiency Enhancement in 40nm Bulk CMOS

In this paper, a series-parallel combined Doherty power amplifier is proposed. The Doherty architecture is adopted for PBO efficiency enhancement. The power-combining structure is applied for the improvement of saturated output power. The PA is designed in a 40nm bulk CMOS process to achieve a saturated output power of 23dBm and a peak power-added efficiency of 40%. The 6-dB back-off power-added efficiency achieves 26%, indicating obvious efficiency improvement owing to Doherty architecture. The 3-dB bandwidth of the proposed power amplifier is over 6GHz, and OP1dB exceeds 20 dBm in 25–31 GHz.
Radiation Pattern Decoupling of Low-Profile Substrate Integrated Dielectric Resonator Antennas

A decoupled $1\times 2$ cylindrical substrate integrated di-electric resonator antennas (DRAs) is presented using metallic vias and rectangular strips. The two externally tangent cylindrical DRAs are implemented in a printed circuit board (PCB) with two different substrate layers, where the upper and the lower sub-strates are used for antenna structure and feed network, respectively. By inserting vertical metallic vias into the upper substrate and attaching horizontal strips at both sides of the intersection, reduced isolation and distorted radiation pattern caused by mu-tual coupling is corrected, resulting in symmetric radiation pat-terns and maximum radiation along z-axis. To verify the idea, a design working at 24.00-24.25 GHz is designed. It can be found that the isolation between the two ports are higher than 15 dB, the envelope correlation coefficient (ECC) is below 0.002, and the maximum radiation is along z-axis, which makes the antenna suit-able for multiple-input and multiple output systems.
Monitoring the Changes of Electroencephalogram During Cardiopulmonary Resuscitation

The electroencephalogram (EEG), a technique that uses scalp electrodes to monitor electrical potential variations in synaptic neuronal activity to provide information on cortical brain function, is extremely sensitive to changes in consciousness and has shown promising results in brain injury and functional recovery after cardiac arrest (CA). However, changes of the EEG signals during cardiopulmonary resuscitation (CPR) are still unclear. This study aimed to develop an approach to monitoring the EEG alterations during CPR and examine the effects of CPR on the cerebral cortical function. A CA model was realized on five healthy adult Bama pigs. A standard CPR treatment was performed on the pigs, by which four of them fulfilled the standard of return of spontaneous circulation (ROSC) whereas one of them failed to be recovered. During the CPR treatment, 12 channels of EEG were recorded on the global area of cortical area. The power spectral density, the average weighted clustering coefficient (C), and the characteristic path length (L) of the EEG signals were calculated. This study revealed that the ß band is the characteristic band of CPR. Before CPR and after ROSC, the brain networks had more information processing capability, and the values of C and L would serve as good functional indicators for the neurological prognosis by CA.
A Concatenated Cyclic Shift Scheme for PAPR Reduction in DCO-OFDM VLC Systems

In visible light communication (VLC) systems, direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) is a promising multicarrier modulation technique. However, high peak-to-average power ratio (PAPR) is a major challenge for DCO-OFDM VLC systems. As an efficient technique, selected mapping (SLM) was proposed to solve this problem. In this paper, we propose a SLM-based concatenated cyclic shift (CCS) scheme, which can generate candidate sequences by linearly combing time-domain sequences with concatenated cyclic shift. It can generate sequences with different PAPR values, thus, it is more efficient in generating lower PAPR sequences. Simulation results show that the proposed method can achieve superior performance in terms of PAPR and BER than conventional cyclic shift and phase rotation schemes.
Time Efficient Calculation of Current Harmonics in Inverter-Fed Electrically Excited Synchronous Machines

The prediction of the time slope of the stator and rotor currents in an inverter-fed electrically excited synchronous machine (ESM) is mainly carried out using models of transient co-simulation with time stepping finite element method (FEM). In the case of calculating a large number of operating points, this method is very time consuming and computationally expensive. This paper presents a fast and efficient methodology that allows the calculation of these currents in a fraction of the usual time. Steady state operating points can be calculated directly with less computational expense and using open-source software.A comparison with one of the already frequently validated transient co-simulation models for an ESM proves the applicability of the methodology and at the same time shows which potential it has for extension.