Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Segmentation Network of Remote Sensing Images With Dynamic Loss Fusion Strategy

The remote sensing (RS) images are widely used in various industries, among which semantic segmentation of RS images is a common research direction. At the same time, because of the complexity of target information and the high similarity of features between the classes, this task is very challenging. In recent years, semantic segmentation algorithms of RS images have emerged in an endless stream, but most of them are improved around the scale features of the target, and the accuracy has great room for improvement. In this case, we propose a semantic segmentation framework for RS images with dynamic perceptual loss. The framework is improved based on the InceptionV-4 network to form a network that includes contextual semantic fusion and dual-channel atrous spatial pyramid pooling (ASPP). The semantic segmentation network is an encoder-decoder structure. In addition, we design a dynamic perceptual loss module and a dynamic loss fusion strategy by further observing the loss changes of the network, so as to better improve the classified details. Finally, experiment on the ISPRS 2D Semantic Labeling Contest Vaihingen Dataset and Massachusetts Building Dataset. Compared with some segmentation networks, our model has excellent performance.
Optimizing Spatiotemporal Feature Learning in 3D Convolutional Neural Networks With Pooling Blocks

Image data contain spatial information only, thus making two-dimensional (2D) Convolutional Neural Networks (CNN) ideal for solving image classification problems. On the other hand, video data contain both spatial and temporal information that must be simultaneously analyzed to solve action recognition problems. 3D CNNs are successfully used for these tasks, but they suffer from their extensive inherent parameter set. Increasing the network’s depth, as is common among 2D CNNs, and hence increasing the number of trainable parameters does not provide a good trade-off between accuracy and complexity of the 3D CNN. In this work, we propose Pooling Block (PB) as an enhanced pooling operation for optimizing action recognition by 3D CNNs. PB comprises three kernels of different sizes. The three kernels simultaneously sub-sample feature maps, and the outputs are concatenated into a single output vector. We compare our approach with three benchmark 3D CNNs (C3D, I3D, and Asymmetric 3D CNN) and three datasets (HMDB51, UCF101, and Kinetics 400). Our PB method yields significant improvement in 3D CNN performance with a comparatively small increase in the number of trainable parameters. We further investigate (1) the effect of video frame dimension and (2) the effect of the number of video frames on the performance of 3D CNNs using C3D as the benchmark.
Independent Component Analysis and Extended Kalman Filter for ECG signal filtering

During acquisition or transmission of the Electrocardiogram, noises generated from the surrounded electrical equipment, the patient’s motion, movement of the electrodes, or contraction of the muscle around the heart usually interfere with the obtained signal. The interference of these noises in the frequency domain may mask the desired signal and obstruct the diagnosis process. Blind Source Separation techniques and Model-based filtering methods have shown promising results in ECG signal processing. This work pointed to assess the performance of Independent Component Analysis and Extended Kalman Filter in removing the most common ECG noise, such as muscle contraction, baseline shift, and electrode motion artifact. Testing has been executed on a formed signal set by adding noises from the MIT noise stress test database to signals from the MIT-BIH arrhythmia database at a different signal to noise ratio. Performance comparison demonstrates that both techniques show satisfying results in muscle artifact filtering, while ICA based filtration is more accurate than EKF in reducing baseline wander and electrode movement artifacts.
Trajectory Tracking for the Quadcopter UAV utilizing Fuzzy PID Control Approach

Currently, the quadcopter Unmanned Aerial Vehicles (UAVs) are playing a significant role in combating the COVID-19 pandemic crisis, which induced the researchers to design robust control techniques. In this paper, a fuzzy PID controller is designed to stabilize and/or track the desired trajectory of the quadcopter UAV. The mathematical model of the quadcopter UAV has been briefly presented, where it has been divided into two portions, the position dynamic and the attitude dynamic subsystems. Subsequently, a robust fuzzy PID controller has been designed for both the inner loop and outer loop to control and stabilize the position and the attitude of the quadcopter, which adaptively manipulate the system’s input based on the tracking error. The proposed controller is benchmarked with the conventional PID controller to show the robustness of the fuzzy PID controller. Fuzzy PID controller has been verified through simulation work utilizing Matlab/Simulink, where better performance is achieved compared with the conventional PID controller. It is found that the errors in the quadcopter’s attitude and position have been significantly reduced through using fuzzy PID controller by 70% and 87%, respectively.
Comparison of Deep Learning Techniques for River Streamflow Forecasting

Recently, deep learning (DL) models, especially those based on long short-term memory (LSTM), have demonstrated their superior ability in resolving sequential data problems. This study investigated the performance of six models that belong to the supervised learning category to evaluate the performance of DL models in terms of streamflow forecasting. They include a feed-forward neural network (FFNN), a convolutional neural network (CNN), and four LSTM-based models. Two standard models with just one hidden layer—LSTM and gated recurrent unit (GRU)—are used against two more complex models—the stacked LSTM (StackedLSTM) model and the Bidirectional LSTM (BiLSTM) model. The Red River basin—the largest river basin in the north of Vietnam—was adopted as a case study because of its geographic relevance since Hanoi city—the capital of Vietnam—is located downstream of the Red River. Besides, the input data of these models are the observed data at seven hydrological stations on the three main river branches of the Red River system. This study indicates that the four LSTM-based models exhibited considerably better performance and maintained stability than the FFNN and CNN models. However, the complexity of the StackedLSTM and BiLSTM models is not accompanied by performance improvement because the results of the comparison illustrate that their respective performance is not higher than the two standard models—LSTM and GRU. The findings of this study present that LSTM-based models can reach impressive forecasts even in the presence of upstream dams and reservoirs. For the streamflow-forecasting problem, the LSTM and GRU models with a simple architecture (one hidden layer) are sufficient to produce highly reliable forecasts while minimizing the computation time.
New Approach to Negative Differential Conductance with High Peak-to-Valley Ratio in Silicon

In this paper we report a new approach to realize negative differential conductance (NDC) with a high peak-to-valley (P/V) ratio in silicon metal-oxide-semiconductor field effect transistors (Si MOSFETs). A P/V ratio of as large as 10 at room temperature has been demonstrated by controlling the effective biasing to p-MOSFETs fabricated on a silicon-on-insulator (SOI) wafer. Although the band-to-band tunneling current is utilized, the excess tunneling current which has ever limited the P/V ratio in the Si Esaki diode is eliminated in the proposed method. Higher P/V ratio will enable us to incorporate NDC functionality into future Si large-scale integrated (LSI) circuits and open up new possibilities for LSI applications.
Investigations on Spectral Photon Radiation Sources to Perform TID Experiments in Micro- and Nano-Electronic Devices

The total ionizing dose (TID) sensitivity of mature and innovative technologies is investigated using both ionizing radiation experiments and Monte-Carlo simulations to discuss the potential of spectral photon radiation sources as an alternative for radiation effects studies. The impact of both technological and radiation test parameters on effective TID deposition is also discussed. All the layers struck by the incident radiation flux before reaching the targeted sensitive oxide are the major contributors of final ionizing dose deposits, rather than the actual sensitive oxide itself. Index Terms—ARACOR, back-end-of-line (BEOL), backside irradiation, Bremsstrahlung, bulk, CMOS, device packaging, dosimeters, front-end-of-line (FEOL), frontside irradiation, gamma, GAMRAY, LINAC, linear accelerator, metal–oxide–semiconductor field effect transistor (MOSFET), ORIATRON, radiation laboratory sources, silicon, silicon-on-insulator (SOI), SiO2, total ionizing dose (TID), X-rays.
A Modular Inductive Wireless Charging Solution for Autonomous Underwater Vehicles

In recent years, autonomous underwater vehicles (AUVs) have gained prominence in the most varied fields of application of underwater missions. The most common solution for recharging their batteries still implies removing them from the water, which is exceptionally costly. The use of Inductive Power Transfer (IPT) technologies allows to mitigate the associated costs and to extend the vehicles’ operation time. In consequence, a prototype has been developed, whose objective is to integrate commercially available IPT technologies, while allowing the employment by most of the AUVs. The prototype is a funnel structure and its counterpart aimed to be fixed to a docking station and the AUV respectively. When coupled, it enables the batteries to recharge by electromagnetic induction. Energy transmission has been tested, resulting in encouraging results. This particular solution achieved over 90% efficiency during underwater experiments. The next objective will be to develop a commercial version of the prototype, that allows a direct, practical and effective use of wireless charging technologies within this particular scenario.
In-home laboratory for learning automation at no cost

In this paper, it is shown the development of a remote lab for distance learning of practical contents, due to the contingency caused by the pandemic of the Covid-19. The proposed remote lad has the advantages of building it without the need for monetary investment and that it can be implemented in a short time. To validate the proposal, a remote lab was set up and used in a couple of courses, Industrial Networks and Logical Automatism, from the Mechatronics Engineering program. After the experimentation and the analysis of the information, it is concluded that the proposal contributes heavily to the student's knowledge, besides generating motivation and engagement in the students. Similar results have been observed between the learning obtained in the classroom format and the one achieved with the use of the proposed remote laboratory.
An empirical evaluation of QoME for NMP

For Network Music Performance (NMP), low end-to-end delay is probably the most critical factor affecting the Quality of Musicians’ Experience (QoME), as inflated delays inhibit the synchronization between musicians. Even though customized NMP tools can greatly reduce application-layer delays, generally available Internet connections impose considerable network-layer delays to NMP sessions in wide-area settings. In order to empirically assess the delay tolerance of NMP, multiple pairs of musicians were asked to perform in a controlled network environment, while we varied the end-to-end delay. The musicians were then asked to evaluate a number of metrics. The statistical analysis of the results indicates that the tolerance of actual musicians to delay is higher than previously thought, thus expanding the applicability of NMP to longer distances.
Ferroelectric order in multiferroic hexagonal manganites

Hexagonal manganites h-RMnO3 (R= Y, Sc, In, Dy–Lu) have attracted significant interest in the past 20 years, mostly because of their multiferroic properties at cryogenic temperature at which electric and magnetic order coexist. Because conventional ferroelectricity and magnetism have complementary origin and thus mutually exclude each other in perovskites, ferroelectricity in h-RMnO3 is intrinsically special. Its geometric mechanism features novel functionalities such as charged domain walls with tunable conductance which are absent in conventional ferroelectrics. However, fundamental aspects of ferroelectricity in h-RMnO3, such as the emergence of spontaneous polarization, are under continuous debate calling for clarification since 50 years. Only recently, microscopic experiments revealed a unique ferroelectric domain structure that features topologically protected domain intersections that wind through the crystal as vortex lines. Hence, ferroelectricity in h-RMnO3 attracts interest for fundamental aspects such as topological defect formation as well as from the perspective of applications. In this thesis, we investigate the origin of ferroelectricity and its unique domain structure by optical second harmonic generation (SHG) and piezoresponse force microscopy (PFM), respectively. First, we compare members of the h-RMnO3 series with the related compound h-InMnO3 emphasizing the geometric nature of the ferroelectric driving mechanism as it relies on the absence of R-O covalency. Next, we apply PFM to reveal the domain structure’s local evolution in h-YMnO3 upon repetitive thermal treatments. Domains form in a narrow temperature range around the structural transition at 1259 K and remain unchanged upon further cooling. However, in contrast to conventional ferroelectrics, oxygen-deficient conditions boost the domain wall mobility promoting domain relaxation with vortex-antivortex annihilation. To address the longstanding debate on the onset of ferroelectricity in h-YMnO3, we utilize symmetry-sensitive SHG to trace the temperature evolution of the spontaneous polarization Ps from 100 K to 1400 K. Our results evidence the single-phase-transition scenario in which Ps emerges simultaneously with the symmetry-breaking lattice distortion at 1259 K. Additional Monte-Carlo simulations of a six-state clock model provide a microscopic picture for the unusual slow evolution of Ps and the domain formation process. Finally, we explore the topological properties of the ferroelectric domain structure and test h-RMnO3 as candidate system for the Kibble-Zurek mechanism which originates from cosmology and delivers a universal scaling law for topological defect formation. Cooling-rate-dependent annealing experiments on h-YMnO3 and h-ErMnO3 evidence the presence of Kibble-Zurek scaling for ferroelectric vortices and thus provide a reliable control of the topological defect formation. Furthermore, we observe a crossover into an unexpected anti-Kibble-Zurek regime with opposite scaling above a compounddependent critical cooling rate.
Performance Evaluation of NOMA-Based Cognitive Integrated Satellite Terrestrial Relay Networks With Primary Interference

Satellite communication has attracted great interests due to extensive coverage and no terrain restriction, which can make up for the shortcomings of terrestrial communication, especially in remote areas. Hence, integrated satellite-terrestrial relay network (ISTRN) is considered as a promising research direction. However, the shortage of spectrum resources is becoming the bottleneck of the future development of ISTRN because of the low utilization of spectrum. In this regard, non-orthogonal multiple access (NOMA) scheme and cognitive radio are considered as great ways to solve this problem. This paper investigates the performance of a NOMA-based ISTRN under spectrum sharing environment with primary interference. Particularly, the exact closed-form expressions of outage probability and ergodic capacity are derived to evaluate the system performance, where the interference constraint caused by the multiple primary users and the interference of primary transmitter have been considered. Furthermore, in order to reveal the impacts of key parameters on system performance at high signal-to-noise ratios efficiently, the asymptotic analysis of OP is also provided. After that, we obtain the relationship between system performance and the power allocation factor, the number of primary users, interference parameter or the channel environment, which will enlighten the research of NOMA-based cognitive ISTRN. At last, Monte Carlo results are provided to verify the effectiveness of our theoretical analysis.
Nanoelectromechanical-Switch-Based Binary Content-Addressable Memory (NEMBCAM)

This paper proposes a novel nanoelectromechanical-switch-based binary content-addressable memory (NEMBCAM) for the first time. A nanoelectromechanical (NEM) memory switch serves as both a nonvolatile memory and a switchable current path in an NEMBCAM unit cell. Owing to monolithic three-dimensional integration, NEMBCAM achieves a smaller unit cell area in comparison with conventional complementary metal–oxide–semiconductor-only binary content-addressable memory. Small unit cell area results in reduced match line (ML) capacitance, which lowers search energy consumption. Furthermore, a shorter search delay is achieved owing to the near-perfect on/off characteristic of the NEM memory switch.
A Data-Driven Automatic Design Method for Electric Machines Based on Reinforcement Learning and Evolutionary Optimization

The design problems of electric machines are actually treated as a kind of mixed-integer problem, because the machine shapes are defined by some integer variables, such as number of slots, and the other variables, such as the tooth width, which are here called the fundamental and shape variables, respectively. To automatically solve these design problems, this article presents an automatic design method by combining the reinforcement learning and evolutionary optimization. In the proposed method, the decision process is modeled as a tree structure to seek for the fundamental variables, which are determined as a result of the tree search depending on the value functions of the nodes. Then, the shape variables are estimated from the function of the fundamental variables. These functions are constructed based on the design data, to generate which the reinforcement learning and evolutionary optimization are employed. As a result, the proposed method can automatically be adapted to unexperienced design problems through the data generation and function learning. The proposed method is applied to a design problem of a linear induction motor. It is shown that the machine designs with the prescribed performance for given specifications are automatically obtained. Moreover, it is also shown that the acceptable candidate designs can immediately be generated when the given specification is similar to the previously-solved problems by utilizing the design data generated by the past explorations.
Advanced Control of Switched Reluctance Motors (SRMs): A Review on Current Regulation, Torque Control and Vibration Suppression

With the increasing environmental concerns, a paradigm shift towards electric and hybrid electric vehicles is expected. Switched Reluctance Motors (SRMs) have emerged as a viable competitor to other established electrical machines. SRMs are known for their simple construction, robustness, inherent fault tolerant structure and low production and maintenance costs. Moreover, the machine has gained interest due to the absence of permanent magnets or windings in the rotor structure, which significantly reduces production costs when compared to other electric motors. The SRM, however, present some known drawbacks, such as increased torque ripple and acoustic noise production, as well as a highly nonlinear behavior. Through the use of adequate control strategies, however, the main challenges of the machine can be overcome. Thus, this paper presents a state-of-the-art review of the advanced control of SRMs, encompassing current regulation strategies, torque control strategies and vibration suppression techniques. First, two categories of current controllers are reviewed: model-independent and model-based. Next, indirect and direct torque control methods are explored. Then, three approaches to vibration suppression are discussed, namely active cancellation, current profiling and direct instantaneous force control. Lastly, a summary of each topic is presented and suggestions of future research topics are listed.
A Heavy-Ion Beam Monitor Based on 3-D NAND Flash Memories

A heavy-ion beam monitor based on 3-D NAND flash memories was designed and tested with heavy ions at high energy and low linear energy transfer (LET). The capability of measuring fluence, angle, uniformity, and LET of impinging particles is discussed, together with the advantages over SRAM-based implementations. We propose ad hoc algorithms for the extraction of the beam parameters, based only on user-mode commands. A validation of the system using low-LET ionizing particles impinging at different angles is presented. Experimental results show very good efficiency and accuracy.
Embedded Residual Recurrent Network and Graph Search for the Segmentation of Retinal Layer Boundaries in Optical Coherence Tomography

For the study of various retinal diseases, an accurate quantitative analysis of the retinal layer is essential for assessing the severity of the disease and diagnosing the progression of the disease. Optical coherence tomography (OCT) images can clearly show each layer of the retinal structure and detect subtle early lesions, thus providing a gold standard for the diagnosis of retinal diseases. In this article, we propose a coarse-to-fine retinal layer boundary segmentation method based on the embedded residual recurrent network (ERR-Net) and the graph search (GS). Considering the integrity of information transmission and the dependence of features, we first design a novel end-to-end residual recurrent network to roughly segment the retinal layer boundaries. The proposed ERR-Net not only solves the gradient problem brought by the depth but also fully captures the global spatial structure of the image. Second, we employ a GS algorithm for boundary continuous to make the layer boundary segmentation results more accurate. Finally, we evaluate the effectiveness of the proposed method on three publicly available datasets and compare it with the state-of-the-art methods on each dataset. The quantitative results and visual effects show that the proposed method outperforms the state-of-the-art approaches in the segmentation of retinal layer boundaries in OCT images. Moreover, the results also show that the proposed method has good algorithm stability on three datasets with different sizes, characteristics, and segmentation difficulties.