Miracle-dz/UniFAR-FTUs huggingface dataset:
Tacking Control of Piezoelectric Stick-Slip Actuator Based on Integral Terminal Sliding Mode Strategy

Piezoelectric stick-slip actuators have influences such as model uncertainty, nonlinear factors and external disturbances that reduce control accuracy. In this paper, an integral terminal sliding mode controller is proposed for the precise positioning control of piezoelectric stick-slip actuators. The method can achieve convergence in finite time and serve to reduce the jitter steady-state error. The convergence law using a combination of power convergence law and exponential convergence law can reach the sliding mode surface at a faster rate. The stability of the control method is verified by the Lyapunov method. Finally, the effectiveness of the modified control method is verified by simulation. The results show that the control method improves the dynamic performance and stability accuracy of the system more than the traditional sliding mode controller.
Investigation on Bandwidth Enhancement in Copper—Carbon Nanotube Composite Interconnects

Based on the equivalent circuit model, this research suggests an efficient technique to increase 3dB bandwidth of Copper-Carbon Nanotube composite interconnects. This technology is widely used inductance peak technology. Additionally, the electrical equivalent circuit model's the transfer function is generated using the ABCD parameter matrix approach. The relationship between the 3dB bandwidth of the interconnect at the global level for the 14nm technology node and the peaking inductance has been determined, taking into account the impacts of length, diameter and load resistance. The simulation results show that the bandwidth may be properly increased by increasing a peak inductance at the load side, but it can not be increased indefinitely. When the inductance is increased beyond a certain value, the bandwidth will remain stable. Also, the overshoot phenomenon will appear for larger inductance.
Replay-Oriented Gradient Projection Memory for Continual Learning in Medical Scenarios

Despite the tremendous progress recently achieved by deep learning (DL) in medical image analysis, most DL models only concentrate on single data distribution, which follows the independent and identically distributed (i.i.d) assumption. However, in practice, image data distribution changes with clinical conditions, such as different scanner manufacturers, imaging settings, and statistics regions. Although one can further train the model on new data samples, updating a model with data from an unknown distribution will always result in the model’s performance degradation on the learned data, a notorious phenomenon called catastrophic forgetting. Therefore affects the applicability of DL algorithms in continuously changing clinical scenarios. In this study, we have proposed a new method to address the impact of changing distributions in continual learning scenarios and alleviate catastrophic forgetting. A gradient regularization approach is used to suppress forgetting, and a replay-oriented consistency calculation method combined with a subspace weighting strategy is proposed to improve the model plasticity further. The proposed replay-oriented gradient projection memory (RO-GPM) is evaluated on multiple fundus disease diagnosis datasets including a real-world application and a continual learning benchmark. The quantitative and visualization results demonstrate that the proposed RO-GPM achieves superior performance to state-of-the-art algorithms by a large margin.1
Engaging the social in engineering

The inclusion of complementary studies courses in undergraduate engineering curricula was advocated and promoted by accreditation bodies worldwide to 'augment and broaden’ engineering education [1]. This can be understood in terms of ongoing efforts to graduate holistic engineers and has resulted in innovations in curricula globally [2]. In South Africa, complementary studies courses are often seen as providing 'soft skills'-although discussion about the value of these courses seems to result in polarised views from staff and students [3], [4]. Given that engineering science persists as the dominant discourse in engineering education [5], this conceptual paper draws on various theoretical tools to explore the development of a more inclusive approach to complementary studies. Starting from the notion of discourse, we draw on the concepts of structure and the hidden curriculum to provide a more substantive way to think about what constrains student engagement with complementary studies courses. We conclude with proposed engagements to extend a positive hidden curriculum emphasising the socio-technical engineering discourse.
Forward scatter shadow ratio for passive forward scatter radar

In a forward scatter radar system, the forward scatter shadow ratio to characterize a target is introduced as the ratio of the total received power density to the incident power density for a receiver at a certain location. Numerical simulations show that the forward scatter shadow ratio is related to the size of the target as well as the position of the receiver relative to the target. It is a more suitable parameter for studying target detectability than the radar cross-section.
Estimating anomalies from indirect observations

Indirect observations often lead to ill-posed inverse problems, where the use of prior information plays a crucial role. When correctly used, informative priors should be based on our belief of what the typical situation looks like, while the aim of the measurement procedure is often to find deviations from the typical. This work proposes a method of implementing priors that on one hand reflects the belief concerning the typical situation and on the other retains sensitivity to anomalies of the prescribed type. Numerical results are based on Bayesian sampling methods.
The boxy bulge and stellar bar of the Milky Way

Abstract We describe the boxy bulge, long stellar bar and elliptical ring of the Galaxy. This model has largely evolved from NIR survey work by many teams and differs from other models with a monolithic ellipsoidal bulge. We maintain that the structure of the inner Galaxy can only be properly studied by adequately sampling the entire Galactic plane for |l| < 30°, |b| < 1.5°, and that the bulge is best studied at least 3° from the plane. We briefly report a slight radially outwardly increasing metallicity gradient along the bar and reaffirm de Vaucouleurs & Pence's (1978) suggestion that the Galaxy is probably of morphological type SAB(rs)bc II.
Spatial and temporal characteristics of modulated waves in the circular Couette system

We have used flow-visualization and spectral techniques to study the spatial and temporal properties of the flow that precedes the onset of weak turbulence in a fluid contained between concentric cylinders with the inner cylinder rotating (the circular Couette system). The first three flow regimes encountered as the Reynolds number is increased from zero are well-known – Couette flow, Taylor-vortex flow, and wavyvortex flow. The present study concerns the doubly periodic regime that follows the (singly periodic) wavy-vortex-flow regime. Wavy-vortex flow is characterized by a single frequency f1, which is the frequency of travelling azimuthal waves passing a point of observation in the laboratory. The doubly periodic regime was discovered in studies of power spectra several years ago, but the fluid motion corresponding to the second frequency f2 was not identified. We have found that f2 corresponds to a modulation of the azimuthal waves; the modulation can be observed visually as a periodic flattening of the wavy-vortex outflow boundaries. Moreover, in addition to the previously observed doubly periodic flow state, we have discovered 11 more doubly periodic flow states. Each state can be labelled with two integers m and k, which are simply related to physical characteristics of the flow: m is the number of azimuthal waves, and k is related to the phase angle between the modulation of successive azimuthal waves by Δϕ = 2πk/m. This expression for the phase angle was first conjectured from the flow-visualization measurements and then tested to an accuracy of 0·01π in spectral measurements. Recently Rand (1981) has used dynamical-systems concepts and symmetry considerations to derive predictions about the space–time symmetry of doubly periodic flows in circularly symmetric systems. He predicted that only flows with certain space–time symmetries should be allowed. The observed flow states are in agreement with this theory.
Improving Data Sampling Efficiency of Sensitivity Analysis Based on Bilevel Multi-objective Evolutionary Algorithm

In constrained optimization problems, sensitivity analysis is essential to analyze the effect of varying the constraints on the optimization results. This study proposes a method to improve the sampling efficiency of a sensitivity analysis method using evolutionary computation, named Bilevel Innovization. Since the conventional method considers all the constraint ranges simultaneously, it is hard to sample analytical data efficiently and accurately. On the contrary, the proposed method divides the constraint ranges into small portions, and sequentially shifts the search ranges from small constraints to large. We conduct experiments to investigate the effectiveness of the proposed method on a multi-dimensional knapsack problem. The experimental results demonstrate that the proposed method can obtain useful data samples for the sensitivity analysis more efficiently than the conventional method.
Grasp Control of a Multi-fingered Robot Hand for Cups of Different Sizes using a Visuomotor Integration Model

Herein, we focused on a robot hand control technology based on image recognition. We conducted experiments on a model that calculates the finger joint angle information of the robot hand from an image of a target object. A model of human visuomotor integration and translation has been proposed, and this model consists of modular auto-encoders corresponding to visual and motor information. To verify the effectiveness of this model, we conducted learning experiments with a real four-fingered robot hand with 16 degrees of freedom. We used cups with different bottom diameters and handle lengths as target objects. We grasped the cups with a robot hand and obtained the finger joint angle when grasping the sides of the cup and when holding the top of the cup as motor information. We conducted a learning experiment using depth images as visual information. As a result, we confirmed that the information on the diameter of the cups could be extracted. Even when images with different cup sizes and handle directions, which were excluded in the training data, were input as test data, the system could represent the cup size. In the hand posture calculation experiment, we confirmed that the posture of the robot hand changes according to the size of these untrained cups.
Numerical Simulation and Analysis of the Acoustic Standing Wave Field Stability in Acoustofluidic Microchannel

Actual research presents numerical simulation which has been run to define the acoustic forces and appropriate pressure distribution profile in a rectangular microfluidic channel. Standing acoustic wave was generated by standard IDT pair, formed on different piezoelectric substrates. The main aim of the numerical calculations was analyzing the stability of pressure nodes position for different Lab-chip design factors and operating parameters.
Intelligent System for Automatic Assessment and Interpretation of Disguised Behaviour for Critical Infrastructure Personnel

This work involves the development of an innovative product aimed at critical infrastructure operators that will bring contributions to the respective sectors in terms of the level of research, technological development, and innovation (RDI). Critical infrastructures are essential at both enterprise and national levels. Any issue related to their operability can significantly impact many areas that depend on them. Developing an intelligent system for automatic assessment and interpretation of disguised behavior for critical infrastructure personnel will create the framework for rapidly defining each specific situation and taking the necessary prophylactic measures. The technical solution implements a neural network architecture for classifying human gestures and emotions using data collected from various sensors. The project also establishes a unique combination of the latest biometric and biosignal processing techniques with an NN approach for the near real-time analysis of emotions and behavior.
Towards Skin Cancer Self-Monitoring through an Optimized MobileNet with Coordinate Attention

Skin cancer is one of the most frequent type of cancer, which is tipically divided in two types: melanoma and non-melanoma. Melanoma is the least common, but also the deadliest of them if left untreated in early stages. Thus, skin cancer monitoring is key for early detection, which could be done with the help of mobile devices and artificial intelligence solutions. In this sense, local deployment is suggested to embrace simplicity and avoid data privacy and security issues. However, current high-performance neural networks are extremely challenging to be deployed in mobile devices due to resource constraint, so lighter but effective models are required to make local deployment possible. In this work, simplifying an already light model, such as MobileNetV2, is pursued, combining it with an attention mechanism to enhance the network's capability to learn and compensate for the lack of information that simplifying the original architecture might cause. Fine-tuning was applied, using an autoencoder to pre-train the model on the CIFAR100 dataset. Experiments covering four scenarios were carried out using HAM10000 dataset. Promising results were obtained, reaching the best performance using a simplified MobileNetV2 combined with Coordinate Attention mechanism with less than a million parameters in total and up to a 83.93 % of accuracy.
Defensive Adversarial Training for Enhancing Robustness of ECG based User Identification*

Electrocardiogram (ECG) based user identification has received considerable attention with the advent of wearable devices. It provides emerging applications including personal healthcare a convenient way to authenticate users as the process can be performed at the moment the user makes contact with the device. However, a recent study discovered that injecting noise into the signal transmitted from the user to an application can effectively hinder the classification process. Many efforts have been made to deal with this noise injection attack, but most approaches have focused on noise removal. In contrast, this paper proposes Defensive Adversarial Training (DAT), which involves training a model with various noisy data to enhance the robustness of deep learning-based identification algorithms. We used two types of noise, Gaussian and Laplacian, to create noisy data. In addition, a sliding-window technique was used to effectively extract useful features and to achieve better accuracy. Our simulation results demonstrate that the proposed approach is highly robust to noise injection attacks and even against random noise. A comparative analysis with noise removal schemes also shows that the proposed DAT significantly enhances the robustness of ECG-based user identification.
Almost regular sequences and Betti numbers

Let Δ be a simplicial complex, and Δ s the shifted simplicial complex of Δ, as defined by Kalai. It is shown that the Stanley-Reisner ideals I Δ and I Δ s have the same extremal Betti numbers. This theorem is the squarefree analogue of the corresponding result by Bayer, Charalambous and Popescu which asserts that a graded ideal and its generic ideal have the same extremal Betti numbers. Our theorem implies well-known results by Kalai according to which Δ and Δ s have the same simplicial homology, and Δ is Cohen-Macaulay if and only if its shifted complex is Cohen-Macaulay.
Strong exciton-photon coupling in a microcavity containing layered perovskite semiconductors

The authors have realized a Perot-Fabry microcavity containing a two-dimensional layered perovskite-type semiconductor, (C6H5C2H4–NH3)2PbI4, between a dielectric mirror and a metallic mirror. A strong coupling regime between the perovskite exciton and the confined photon mode has been evidenced at room temperature from angular-resolved reflectivity experiments. An anticrossing of 140meV is observed between the excitonic and cavity modes. (C6H5C2H4–NH3)2PbI4 having a self-organized multiple quantum well structure, they have determined the oscillator strength per quantum well of this structure: 4×1013cm−2, which is one order of magnitude higher than in conventional inorganic quantum wells.
Towards a Tangible Blocky Coding Design for Visually Impaired Children

This paper investigates the role of coding in children's education and the barriers to inclusion for Visually Impaired (VI) children in this environment. In the learning process, these children face numerous challenges, including the fact that most playful coding-related learning options rely on visualization. This paper incorporates insights from a literature review to design a solution involving a model called DODO through computer vision and tangible paper blocky modules for VI children to learn to code. The main result is the proof of concept prototype demonstrates that the proposed design can recognize the various paper modules used to represent different codes, targeting the tactile sense. The design advocates for VI children to have access to economic Science, Technology, Engineering, and Mathematics (STEM) learning.
MFA-DSLNet: Multi-scale Fusion Attention Mechanism with Dual-threshold Self-paced Learning for Pediatric Pneumonia Diagnosis

Chest X-ray (CXR) is crucial to the early diagnosis and treatment of pneumonia in children while still facing the viral and bacterial pneumonia identification challenge. Although convolutional neural networks (CNNs) can identify the concealed features between viral and bacterial pneumonia, the influence on the network accuracy led by morphological lesion size variation and confounded visual imaging findings is not explicitly addressed. To address these issues, we propose a novel framework MFA-DSLNet to classify viral and bacterial CXR in childhood pneumonia. It comprises two functional blocks. First, a Multiscale Fusion Attention (MFA) mechanism fuses low-level and high-level features to enrich the information in feature maps. Then, two attention modules, namely Context Attention (CA) and Spatial Position Attention (SPA), attempt to extract effective latent information from the channel and spatial dimension respectively to tackle the redundancy problem in feature maps. To facilitate the identification of hard cases, Dual-threshold Self-paced Learning (DSL) is proposed to dynamically adapt the training sequence of samples. Empirically, MFA-DSLNet achieves a state-of-the-art 93.33% accuracy, with a 2.33% improvement to the previous works on the ChestXray2017 dataset.
Millimeter-Wave Dual-Band Antenna Array Integrated in Metal-Framed Smartphones

In this work, a millimeter-wave (mmWave) dual-band phased array antenna is proposed for 5G/B5G smartphones. The configuration of the antenna array is integrated in the narrow metal frame of a smartphone. The proposed antenna contains a slotted frame and a cavity-backed antenna module. A prototype of the proposed 1$\times$ 4 phased array assembled in a simplified metal frame is fabricated and measured. The antenna array achieves a -10-dB impedance bandwidth from 24.9-30.4 GHz and 35.5-40.7 GHz, covering 5Gn257/260 band. And the phased array can obtain good scanning performance in the dual bands.
Passive Monitoring of Physiological Precursors of Stress Leveraging Smartwatch Data

Developing the capability to continuously and noninvasively monitor the mental health status of individuals is a critical focus in the mHealth domain. The use of passively-generated data gathered via smart and portable electronic devices to monitor specific indicators of mental health has shown potential to serve as an effective alternative to traditional intrusive survey-based approaches to monitoring mental health remotely. In this study, we propose a remote health monitoring framework for dynamic, flexible, and scalable assessment and detection of physiological precursors of a stress response. Our method comprises a smartwatch-based system for continuous monitoring of primary physiological signals, followed by a deep neural network architecture that performs the fusion and processing of the multi-modal sensor readings. We empirically validate our system on a cohort of university-affiliated members of the military. Our findings demonstrate the effectiveness of our passive-sensing system for tracking perceived stress, the results of which can be used to obtain a better understanding of patient behavior and improve personalized treatments1.
DSHP: A Novel Sequence Based Deep Learning Prediction Model for HPV Integration Site

HPV, a significant hazard to human health, is the primary cause of many cancers. The proteins E6 and E7 of HPV are known to damage oncogenes, but many mechanisms are still unknown. Research reveals that HPV can integrate its genome into host genes, and the integration mechanism strongly depends on the local genomic environment. Research on the integration mechanism can deepen the understanding of HPV and the development of vaccines, thus further affecting the cure of cancers and other related diseases. However, the research on HPV integration sites in silico experiments is in its infancy, and improving the model performance of HPV integration site predictors is challenging. In this work, we propose a novel deep learning model for HPV integration site prediction named DSHP. DSHP uses a variety of features of DNA sequences as input. In the 5-fold cross-validation, the ACC and AUC of DSHP are 0.914 and 0.934; in the 10-fold cross-validation, the ACC and AUC of DSHP are 0.933 and 0.941. The performance fully illustrates the effectiveness of DSHP. Moreover, our ablation experiments further explain the importance of features in the prediction process, and provide a reference for future prediction research. The data and code are available at: https://github.com/xtnenu/DSHP.
Markov Guided Spatio-Temporal Networks for Brain Image Classification*

This paper proposes a representation learning model to identify task-state fMRIs for knowledge-concept recognition, which has the potential to model the human cognitive expression system. The traditional CNN-LSTM is usually employed to learn deep features from fMRIs, where CNN aims at extracting the spatial structure and LSTM accounts for the temporal structure. However, the manifold smoothness of the latent features caused by the fMRI sequence is often ignored, leading to unsteady data representation. In this paper, we model latent features as a hidden Markov chain and introduce a Markov-guided Spatio-Temporal Network (MSTNet) for brain image representation. Concretely, MSTNet has three parts: CNN that aims to learn latent features from 3D fMRI frames where a Markov Regularization enforces the neighborhood frames to have similar features, LSTM integrates all frames of an fMRI sequence into a feature vector and fully connected network (FCN) that is to implement the brain image classification. Our model is trained towards minimizing the cross entropy (CE) loss. Our experiment is conducted on the brain fMRI datasets achieved by scanning college students when they were learning five concepts of computer science. The results show that the proposed MSTNet can benefit from the introduced Markov regularization and thus result in improved performance on the brain activity classification. This study not only shows an effective fMRI classification model with Markov regularization but also provides the potential to understand brain intelligence and help patients with language disabilities.