Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Overlapping Area Determination and Segmentation for Multiple Side Scan Sonar Images Mosaic

Seafloor image is important for marine scientific research and ocean engineering. With the wide use of side scan sonar (SSS), multiple measured SSS images need to be mosaicked to form a large-scale and high-resolution seafloor image. This article proposes an automatic mosaic method. First, the overlapping areas between adjacent strips are determined automatically by the track lines and swath width. In the overlapping areas, the image matching is conducted using the speeded-up robust features algorithm with the constraint of geographic coordinates of detected feature points. Then, the overlapping area is segmented using the k-means cluster method. Within each segmented region, the coordinate transformation model is established to make the positions of common features in the overlapping areas unique. With regards to multiple SSS images mosaic, the method to mosaic in sequence is also proposed according to the permutation parameter of each strip. Finally, the SSS images in the overlapping areas are fused and a large-scale seafloor image is formed. This proposed method was applied in one water area of Shenzhen, demonstrating good performance in terms of coordinate consistency of the same features in the mosaicked image. The mean coordinate deviations of the same feature points in overlapping areas were nearly zero and the standard deviation of them also decreased. This proposed method is easily transferable to other study areas and provides an objective, repeatable means for multiple SSS images mosaic.
Comparison of Electric Machine Types for Electrically Driven Engine Accessories Using Multiphysics Simulation Tools

With growing interest in electrification in automotive industries, the selection of suitable electric machine types based on application requirements is most important. In this article, three electric machine types, axial flux machine, interior permanent magnet (IPM) machine, and switched reluctance machine, are compared systematically to find the best candidate to drive engine accessories in a hybrid truck. These machine types are designed and optimized to meet the application-oriented design requirements and constraints. An extensive Pugh matrix is created where each machine type is scored based on efficiency range, cost, volumetric power density, gravimetric power density, acoustic noise and stress on the rotor, fault tolerance, heat rejection, and manufacturability. Each Pugh matrix component is assigned to a priority-based weighting, based on the application demand, to determine an accumulated score for each machine topology. A full-scale prototype of the best performing machine type is built and tested. Experimental results validate the multiphysics conclusions drawn in the work.
Technology-Assisted Emotion Recognition for Autism Spectrum Disorder (ASD) Children: A Systematic Literature Review

The information about affective states in individuals with autism spectrum disorder (ASD) is difficult to obtain as they usually suffer from deficits in facial expression. Affective state conditions of individuals with ASD were associated with impaired regulation of speech, communication, and social skills leading towards poor socio-emotion interaction. It is conceivable that the advance of technology could offer a psychophysiological alternative modality, particularly useful in persons who cannot verbally communicate their emotions as affective states such as individuals with ASD. The study is focusing on the investigation of technology-assisted approach and its relationship to affective states recognition. A systematic review was executed to summarize relevant research that involved technology-assisted implementation to identify the affective states of individuals with ASD using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. The output from the online search process obtained from six publication databases on relevant studies published up to 31 July 2020 was analyzed. Out of 391 publications retrieved, 20 papers met the inclusion and exclusion criteria set in prior. Data were synthesized narratively despite methodological and heterogeneity variations. In this review, some research methods, systems, equipment and models to address all the related issues to the technology-assisted and affective states concerned were presented. As for the consequence, it can be assumed that the emotion recognition with assisted by technology, for evaluating and classifying affective states could help to improve efficacy in therapy sessions between therapists and individuals with ASD. This review will serve as a concise reference for providing general overviews of the current state-of-the-art studies in this area for practitioners, as well as for experienced researchers who are searching for a new direction for future works.
Sentiment Classification Algorithm Based on Multi-Modal Social Media Text Information

The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper. It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale. Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked. The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time. The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on. However, such models are limited by the structure and have a low classification accuracy. In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time. This paper proposed a sentiment classification algorithm based on multi-modal social media text information. The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN). The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data. Experiments verify that the training time of this model is slightly less than TextRNN. The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and small-scale data sentiment classification.
Quench Protection Studies for the High Luminosity LHC Nb$_3$Sn Quadrupole Magnets

Achieving the targets of the High Luminosity LHC project requires the installation of new inner triplet magnet circuits for the final focusing of the particle beams on each side of the two main interaction points. Each of the four circuits will include six 150 mm aperture, 132.2 T/m gradient, Nb$_3$Sn quadrupole magnets to be installed in the LHC tunnel. The recently updated circuit topology is such that the protection of each magnet can be studied from a single magnet point-of-view. To limit the hot-spot temperature and the peak voltage-to-ground, a protection system was designed that quickly and reliably transfers voluminous parts of the coil to the normal-conducting state, hence distributing more homogeneously the magnets stored energy in the windings. This system is based on two elements: quench heaters attached to the outer layers of the magnet coils and CLIQ (Coupling-Loss Induced Quench). The performance of the protection system is investigated by simulating the electro-magnetic and thermal transients occurring after a quench with the program STEAM-LEDET, and by conducting dedicated experiments at the CERN and FNAL magnet test facilities. The effectiveness of the quench protection system is assessed at all representative operating current levels. Furthermore, the coils hot-spot temperature and peak voltage to ground are analyzed for various failure cases, conductor parameters, and parameter distribution among the four coils. It is concluded that the proposed design assures an effective, reliable, and fully redundant quench protection system.
Double Stage Solar PV Array Fed Water Pump Driven by Permanent Magnet Synchronous Motor

This article presents an improved standalone double stage solar photovoltaic (PV) array powered water pumping system with protection capability. The proposed system utilizes a solar PV array boost dc–dc converter, three-phase voltage source inverter, permanent magnet synchronous motor (PMSM), and a centrifugal pump, where the boost converter serves dual purposes. The first purpose is of maximum power point tracking by adjusting the duty ratio using an incremental conductance (INC) method in order to extract optimum power from the PV array, whereas the second purpose that it serves is system protection, which has been proposed for the first time. A modified INC algorithm with the capability of system protection is proposed for this purpose. The PMSM is driven in vector control mode and drives the pump. A power feed-forward term is utilized, which improves the system dynamic response. In this way, the purpose of effective and efficient water pumping is fulfilled. The proposed system is modeled using MATLAB/Simulink environment and its performance is validated through simulation and hardware validation by studying its behavior under varying insolation levels.
High-Precision Temperature-Compensated Magnetic Field Sensor Based on Optoelectronic Oscillator

We have proposed and experimentally demonstrated a high-precision and temperature-compensated magnetic field sensor based on two optoelectronic oscillators (OEOs) incorporating cascaded magnetostrictive alloy-fiber Bragg grating (MA-FBG) and FBG filters. The optical sources are provided by the reflection signals of two FBGs. The optical source wavelength is related to the overall time delay of the OEO loop which determines the oscillating frequency of OEO. The central wavelengths of MA-FBG and FBG change along with the magnetic field and temperature. Therefore, we can estimate the magnetic field and temperature by measuring the oscillating frequencies of OEOs, simultaneously. Besides, since two OEOs are grouped into a cross-referencing structure by wavelength division multiplexing under the same environment, the magnetic field sensor can reduce the influence of temperature. The proposed sensor for magnetic field and temperature with sensitivities of 250 Hz/Oe and 1.97 or 1.42 kHz/°C are achieved. The maximum measurement errors of magnetic field and temperature obtained are within 22 Oe and 0.2°C. The proposed scheme has merits of simplicity and compact configuration. It can realize high-speed and high-resolution measurement, which has potential applications in magnetic field sensing.
Efficient Computing of PageRank Scores on Exact Expected Transition Matrix of Large Uncertain Graph

Ranking nodes in uncertain graph is computationally expensive when the graph is huge due to the extremely large number of possible worlds. Some approximation is needed in general. We focus on PageRank centrality measure to rank and propose a method that does not use any approximation for uncertain graph in which all the links can be uncertain. We first compute the expected transition matrix over all the possible graphs accurately and then run PageRank algorithm only once to rank the nodes (p-avg approach). This is not the same as computing the scores for each individual graph first and then rank the nodes by taking their average (s-avg approach). Exact computation of the latter is not possible because of the heavy computational load and only the approximate scores are obtained by limiting the number of graphs by sampling. We have tested the performance from various angles using three real world networks. We show that the proposed method (p-avg approach) gives very high precision to the s-avg approach for highly ranked nodes and can be a good alternative to it. Pactically, the p-avg approach runs orders of magnitude, i.e., sample size, faster than the s-avg approach.
High Q Lateral-Field-Excited Bulk Resonator Based on Single-Crystal LiTaO₃ for 5G Wireless Communication

The paper presents the design and fabrication of lateral-field-excited (LFE) resonators based on 42° Y-cut single-crystal LiTaO<sub>3</sub> (LT) on silicon dioxide (SiO<sub>2</sub>). A simple coplanar top electrode is defined to excite the bulk acoustic wave modes in the suspended LT/SiO<sub>2</sub> structure, and the fabrication process that only involves two lithography steps is more simplified compared to that of commercial film bulk acoustic wave resonators. For a model structure consisting of LT(670 nm)/SiO<sub>2</sub> (1500 nm) thin film, two types of acoustic modes are both piezoelectrically active in the LT film: the first one is the thickness-shear mode with a resonance frequency of 2.46 GHz, an electromechanical coupling (<inline-formula> <tex-math notation="LaTeX">$k_{eff}^{2}$ </tex-math></inline-formula>) of 1.4%, a high quality factor (<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>) of 1690, and the second one corresponds to longitudinal mode with a resonance frequency of 4.39 GHz, <inline-formula> <tex-math notation="LaTeX">${k} _{eff}^{2}$ </tex-math></inline-formula> of 1.2%, a high <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> of 1590, which is among the highest reported for piezoelectric MEMS resonators operating at this frequency range. The excellent performance would enable application scenarios including high-resolution sensors, low-phase-noise oscillators, and low-loss, high selectivity filters in the sub-6 GHz range for the fifth-generation (5G) wireless communication.
Multi-Objective Optimization Method to Search for the Optimal Convolutional Neural Network Architecture for Long-Term Structural Health Monitoring

To ensure the safety of structures, structural health monitoring (SHM) techniques that use cutting-edge sensing technologies have been developed. However, in the process of long-term structural health monitoring, sensor defects and data loss commonly occur, which pose limitations in the current SHM technique. To recover lost data and predict structural responses, convolutional neural networks (CNNs) have been used in SHM, but no obvious technique or rule for configuring CNN architecture with optimal performance has been presented yet. This study proposes a method for searching for the optimal CNN architecture capable of predicting the structural response of structures to evaluate their long-term safety. In this method, multi-objective optimization, considering both prediction performance and CNN training efficiency, is presented as a strategy. The optimization method using the two objective functions is applied to the structural response estimation, and the characteristics of the derived solutions are examined. Furthermore, the solutions derived using the two objective functions are classified into two solution groups that are biased to each objective function, and a strategy for minimizing the discrepancy between the two solution groups is additionally presented based on their trade-off relationship. The architecture characteristics, estimation performance, and training efficiency of the solutions derived by setting the discrepancy as the third objective function are investigated. The CNN derived by the proposed method with the third objective function reduced 40.35% of computational cost compared with that derived with two objective functions while they showed similar accuracies for the response estimation.
Gender domain adaptation for automatic speech recognition

This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conducted experiments with finetuning on the gender-specific test subsets. The obtained word error rate (WER) relatively to the baseline is up to 5% and 3% lower on male and female subsets, respectively, if the layers in the encoder and decoder are not frozen, and the tuning is started from the last checkpoints. Moreover, we adapted our base model on the complete L2 Arctic dataset of accented speech and finetuned it for particular speakers and male and female genders separately. The models trained on the gender subsets obtained 1–2% lower WER when compared to the model tuned on the whole L2 Arctic dataset. Finally, it was experimentally confirmed that the concatenation of the pretrained voice embeddings (x-vector) and embeddings from a conventional encoder cannot significantly improve the speech recognition accuracy.
Scalable Blind Matching: An Efficient Ciphertext Matching Scheme for Content-Based Pub/Sub Cloud Services

Content-based publish/subscribe cloud services are prevailing recently. Confidentiality in publish/subscribe cloud services has become a major concern, especially for applications with sensitive data. Many methods have been proposed to achieve the confidentiality of events and subscriptions. Unfortunately, all these approaches suffer significant performance deterioration while matching on large-scale subscription set. In this paper, we propose an efficient ciphertext matching scheme called SBM. To the best of our knowledge, SBM is the first approach which can preserve confidentiality while at the same time provide scalable event matching. Furthermore, we have integrated SBM with an open source publish/subscribe middleware, PADRES and conducted extensive experiments to evaluate our scheme. The experimental results demonstrate that the matching speed of our solution is faster by two orders of magnitude than its counterparts.
Understanding Bit-Error Trade-off of Transform-based Lossy Compression on Electrocardiogram Signals

The growing demand for recording longer ECG signals to improve the effectiveness of IoT-enabled remote clinical healthcare is contributing large amounts of ECG data. While lossy compression techniques have shown potential in significantly lowering the amount of data, investigation on how to trade-off between data reduction and data fidelity on ECG data received relatively less attention. This paper gives insight into the power of lossy compression to ECG signals by balancing between data quality and compression ratio. We evaluate the performance of transformed-based lossy compressions on the ECG datasets collected from the Biosemi ActiveTwo devices. Our experimental results indicate that ECG data exhibit high energy compaction property through transformations like DCT and DWT, thus could improve compression ratios significantly without hurting data fidelity much. More importantly, we evaluate the effect of lossy compression on ECG signals by validating the R-peak in the QRS complex. Our method can obtain low error rates measured in PRD (as low as 0.3) and PSNR (up to 67) using only 5% of the transform coefficients. Therefore, R-peaks in the reconstructed ECG signals are almost identical to ones in the original signals, thus facilitating extended ECG monitoring.
Listen Before Receive (LBR) Assisted Network Access in LAA and WiFi Heterogeneous Networks

The Listen-Before-Talk (LBT) is the main procedure for Licensed Assisted Access (LAA) to accomplish fair and friendly coexistence with other operators or technologies operating over unlicensed spectrum. However, in LBT, the lack of coordination with other existing systems brings challenges in sustaining performance in the coexistence of LAA and WiFi networks. Specifically, the hidden node problem (HNP) and exposed node problem (ENP) cannot be effectively handled when both LAA and WiFi nodes attempt to access the unlicensed spectrum at the same time. Thus, transmission failure might occur and the network performance would be degraded. In order to mitigate the influences caused by HNP and ENP, based on LBT, we firstly analyze HNP and ENP by means of mathematical approach. The analytical results surprisingly reveal that the hidden node and exposed node probabilities are as high as 41% and 39.33%, respectively. Then, a Give And p-persistent Take (GAT) mechanism with the Listen-Before-Receive (LBR) procedure, namely LBR-GAT, is proposed to cope with LBT to reduce the collision caused by HNP as well as to retrieve the bandwidth sacrificed by the ENP. With LBR-GAT, the LAA sender conditionally gives up or takes back transmission opportunities, and thus the unlicensed spectrum could be efficiently shared between LAA and WiFi. Evaluation results show that the proposed LBR-GAT could conditionally obtain better network performance comparing to legacy LBT.
Machine Learning Enabled Wi-Fi Saturation Sensing for Fair Coexistence in Unlicensed Spectrum

In the past few years, machine learning (ML) techniques have been extensively applied to provide efficient solutions to complex wireless network problems. As such, Convolutional Neural Network (CNN) and Q-learning based ML techniques are most popular to achieve harmonized coexistence of Wi-Fi with other co-located technologies such as LTE. In the existing coexistence schemes, a co-located technology selects its transmission time based on the level of Wi-Fi traffic generated in its collision domain which is determined by either sniffing the Wi-Fi packets or using a central coordinator that can communicate with the co-located networks to exchange their status and requirements through a collaboration protocol. However, such approaches for sensing traffic status increase cost, complexity, traffic overhead, and reaction time of the coexistence schemes. As a solution to this problem, this work applies a ML-based approach that is capable to determine the saturation status of a Wi-Fi network based on real-time and over-the-air collection of medium occupation statistics about the Wi-Fi frames without the need for decoding. In particular, inter-frame spacing statistics of Wi-Fi frames are used to develop a CNN model that can determine Wi-Fi network saturation. The results demonstrate that the proposed ML-based approach can accurately classify whether a Wi-Fi network is saturated or not.
DEARP: Dynamic Energy Aware Routing Protocol for Wireless Sensor Network

Sensor network is a set of sensors nodes brought together for multi-hop data transmission to one or more sinks. Wireless sensor networks (WSN) are used in many areas such as smart cities, environmental monitoring, precision agriculture. Once deployed, WSNs are very rigid in terms of reconfiguration. Sofware-Defined Networking (SDN) technology is explored in order to facilitate reconfiguration of WSN nodes. Several architectures have been proposed, among which SDN-WISE. SDN-WISE uses centralized routing model which separates data plane executed by the sensor nodes and the control plane executed by a software program hosted in a controller. In SDN-WISE, data transmission path choice is the best route in terms of the number of hops. Improved variants of SND-WISE use other metrics such as node energy, but the problem with these approaches is that a chosen path is used until one of its nodes depletes its energy before a path change process is initiated. This impacts efficiency of the network and reduces life of the network. In this work, we propose Dynamic Energy Aware Routing Protocol (DEARP) that monitors residual energy of nodes in order to make routing decisions. This will prevent nodes on most stressed paths from running out of its energy quickly while other paths with nodes with higher residual energies could be used. Our approach optimizes lifetime of WSN by preventing most stressed nodes from running out sooner.