Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Transformer Network

Current attention or transform modules in Convolutional Neural Networks (CNNs) are designed pursuing lightweight and in-place. Generally, we need to decrease the channel dimension of input feature maps for reducing computation cost firstly. And then we do some transformation for extracting weight maps or converting to other feature space etc. Finally, we increase the channel dimension back for outputting feature maps with the same size as input. When we change the channel dimension, commonly we choose $1\times 1$ convolutional layers or fully connected layers. They are simple and effective, but need learning parameters and consuming more memory with other computation resources. We propose a novel parameter free method named Channel Transformer Network (CTN) to decrease or increase channels for these modules whilst keeping most information with lower computation complexity. We also introduce a Video Co-segment Attentive Network (VCAN) for person re-identification (ReID) to improve pedestrian’s noticeable representation across multiple video frames. We embed CTN in Non-local, CBAM, COSAM and VCAN blocks to replace $1\times 1$ convolutional or fully connected layers. Experiments of VCAN and CTN embedding models on Mars dataset for person ReID show significant performance in computation efficiency and accuracy, especially VCAN reaches 90.05% in Rank-1. We believe CTN can also be used in other vision tasks like image classification and object detection etc.
Performance-Issues-Mitigation-Techniques for On-Chip-Antennas – Recent Developments in RF, MM-Wave, and Thz Bands With Future Directions

The need for a compact, economical, and low-power wireless system has given birth to the System-on-Chip (SoC) concept viable of a multitude of innovative applications. On-Chip-Antennas (AoCs) are an integral part of the SoC based wireless system and have got a remarkable attention since last one decade. In contrary to off-chip-antennas, AoCs are fabricated and integrated on the same substrate where the other components of the wireless system are fabricated. This integration results in several challenges besides highly desired benefits. The most serious challenge is the degradation in AoC’s key performance metrics, i.e. gain and radiation efficiency because of low-resistivity and high-permittivity of the Silicon substrate which is the optimized choice for the AoC design. This triggers a need for the use of novel methods for the mitigation of these performance issues. Consequently, a variety of such innovative methods have been proposed in the literature. However, a critical description on the recent developments in these methods at one place to facilitate a wide range of researchers is missing. Therefore, this article presents a well-organized and systematic survey on the recent advancements of the AoC’s performance-issues-mitigation-methods for wireless applications in Radio Frequency (RF), Millimeter-Wave (MM-Wave), and Terahertz (THz) bands for the first time. A concise description of future directions with respect to AoCs performance enhancement methods is also part of this article. It is anticipated that the critical presentation of these methods will make this article a valuable guide for a wide spectrum of researchers, who want to adventure with the tough task of high performance AoC design. In addition, it is envisioned that this article will pave a promising path for a further dominance of the AoCs in RF, MM-Wave, and THz regions.
Statistical Feature Extraction and System Identification Algorithms for Partial Discharge Signal Classification Using Laguerre Polynomial Expansion

In this paper, a novel algorithm for partial discharge (PD) pulse waveform analysis and separation based on orthogonal series expansion using Laguerre polynomials is proposed. A system identification technique is also developed to help increase the accuracy rate of the signal classification. Laguerre polynomial series expansions are applied on the PD pulses to effectively extract important information and well-discriminative features. Laguerre expansion coefficients inherently contain information about the amplitude (intensity) and the relaxation time of a dynamic system and this helps direct characterization of PD pulses. The proposed technique is applied to PD pulses acquired from a laboratory set of multiple PD sources. It is shown that this statistical method can classify partial discharge signals with high accuracies, even when the signals are visually indistinguishable. In this research, two methods are developed for system identification. One is based on a deterministic approach in the form of a recursive formula and the other one is a stochastic method based on a group Lasso methodology. The aim of system identification is to improve the classification accuracy by removing the effect of the measurement system from the observed PD waveforms. The accuracy rate of the proposed algorithm is evaluated using the signals that are captured from simultaneous artificial defects in high pressure SF6 and in low pressure air.
Towards Human Motion Tracking: An Open-source Platform based on Multi-sensory Fusion Methods

Human motion tracking (HMT) has been a research focus in the last decades. In this paper, we propose an IMU/TOA-fusion-based platform to solve this problem. Firstly, Time-of-arrival (TOA)-based distance ranging method is considered to compensate for the drifting errors and accumulation introduced by inertial sensors. Secondly, a geometrical kinematic model and maximum correntropy criterion (MCC)-based Kalman filter method are proposed to fuse the multiple information. The open-source hardware and software are detailed in this paper for real-time human motion capture and reconstruction applications. Experiment results show that our proposed hardware can be easily equipped for total body motion reconstruction with a considerable enhancement of the wear-ability and comfort. Furthermore, the main achievements have been presented with a performance comparison between the proposed platform and state-of-the-art commercial ones. Above all, our proposed platform can significantly suppress the accumulative error and drifting problem of conventional inertial systems. More importantly, it realizes the open-source software and hardware, thus it has promising prospects for wearable human motion tracking applications.
LSTM Based Recurrent Enhancement of DQN for Stock Trading

In real stock trading tasks, technical and fundamental analysis are required in order to ensure positive earning at the end of trade. Machine learning approaches are widely used in financial market analysis especially stock price prediction to support the investor decision. However, most of the techniques only provide price forecasting without action recommendation. Reinforcement learning technique facilitates stock trading decision using algorithm that can make decision to maximize profit. In this work, a novel approach based on LSTM as a preprocessing recurrent network to enhance the Deep Q-Network (DQN) is presented. The experiment results promote the robustness of the proposed algorithms and it can be considered as a baseline algorithm for other agent-based stock trading research.
A Residual Attention Aware Network Based Faults Characterization of a Power System with SFCL

Faults in power systems are responsible to increase the current abruptly that gradually damages the protective equipment and loads. Although the use of a superconducting fault current limiter (SFCL) decreases the level of fault current, it considers 2 cycles for detecting the fault where the conventional circuit breaker takes it up to 5 cycles. In this paper, we propose a deep learning network to detect and classify the fault with a fraction of the cycle for the rapid restoration of faulty phases. The decayed current signals using the SFCL are considered to measure the effectiveness of the proposed learning network. The obtained result shows that the proposed learning approach can detect and classify the fault within the fraction of one cycle.
The containment of fake news propagation in online social networks

Fake news has permeated into people's daily life especially with the help of the popularity of online social networks. Hence containment of fake news propagation becomes a vital issue for modern society. While previous research of fake news containment mainly focused on game theory and information diffusion models on the single network, little attention was paid to the containment on the realistic fake news propagation networks as well as multi-networks. Our finding concentrates on realistic propagation networks and tests the effect of re-posting silence and volume control quantitatively. Furthermore, for the multi-network, the containment of these two methods of fake news is also effective.
The expansion of porous glass on the adsorption of non-polar gases

The design and construction of an absolute dilatometer of high sensitivity to measure the small expansions resulting from the adsorption of non-polar gases on to porous glass at liquid-air temperatures are described. The expansions measured with this instrument are the first recorded with a rigid adsorbent for the case in which the adsorption is, with certainty, physical in nature. Experiments in which the gases argon, nitrogen, oxygen, hydrogen and krypton were used as adsorbates are reported. The effect of organic matter adsorbed on to the glass from the atmosphere, of unknown composition, on the expansion characteristics is indicated. A technique for the removal of this organic matter in situ without affecting the area of the glass is described. The results have confirmed the correctness of an equation which has been derived by the author (Yates 1952) (∂F/∂V)T = –3/2K, where ∂V is the volumetric expansion at constant temperature resulting from ∂F, the surface free energy lowering, and where K is the bulk modulus of the adsorbent.
Measurement of QCM Mass Sensitivity Based on Electrodeposition

In this paper, we used a practical method to calculate the mass sensitivity of quartz crystal microbalance (QCM) based on electrodeposition. The QCMs were used widely due to their high mass sensitivity. Therefore, the measurement of QCM mass sensitivity is a vital research direction. The atomic force microscopy (AFM) and X-ray diffraction (XRD) measurements for deposited copper films on the electrode surface were carried out. We calculated three samples of AT-cut 10MHz quartz crystal resonators, and their mass sensitivities were 2.92 × 10−9 g/cm2·Hz, 3.21 × 10−9 g/cm2·Hz and 3.52 × 10−9 g/cm2·Hz. This practical method is helpful to calculate the mass sensitivity of QCM, and it can be extended to calculate other structure QCMs.
Image Pattern Analysis towards Classification of Skin Cancer through Dermoscopic Images

Malignant melanoma is one of the most dangerous forms of skin cancer. Death rates are quite high because of their proliferation ability to other body parts. Melanoma can be cured with early and correct diagnosis therefore there is a significant requirement of a system that can correctly differentiate benign and melanoma lesions. A computer-aided methodology for the classification of benign and melanoma is proposed in this study. To determine the area of interest of the lesion part, dermoscopic skin lesion images are first segmented using k-means clustering. Next, extensive feature extraction is performed on segmented images by using feature descriptors: local binary patterns (LBP), histogram of oriented gradients (HOG), and bag of visual words (BoVW). These features are evaluated on a wide range of classification methodologies. Experimental analysis reveals that BoVwfeatures with support vector machines classifier yield the highest results in terms of 99.8% accuracy, 100% sensitivity, and 99.5% specificity.
Optimal Design and Experimental Verification of FPGA-based MMC-HVDC Controller

The control system structure of modular multilevel converter based high voltage direct current (MMC-HVDC) is complicated. Data acquisition, control computation, and communication among distributed controllers lead to long link delay. The delay will further cause deterioration of the system’s dynamic characteristics and even instability. This paper presents an integrated controller design based on field programmable gate array (FPGA). The parallel features of FPGA are fully utilized to complete the control calculation task and shorten the link delay as much as possible. Based on the real-time digital simulator (RTDS) platform, hardware-in-the-loop experiments are carried out to verify the function of the designed controller. The results show that the designed controller has short link delay and fast response speed, and can be used in the testing of control strategy, control parameters optimization, and so on.
Czech Sign Language Single Hand Alphabet Letters Classification

The paper deals with the classification of images of Czech sign language alphabet, single handed version in particular, without diacritics. The classification is performed by convolution neural network using TensorFlow computational library. Network topology, data acquisition and automatic labelling and obtained results are described in the paper. The accuracy on the test data - captured images of a person not previously seen by the network – was over 87%.
Image Saliency Detection Based on Contrast Feature of Superpixel Patch

Saliency detection based on visual attention mechanism is one of the current researches focuses of computer vision. In this paper, an image saliency detection algorithm based on the contrast feature of superpixel patch is proposed. First, RGB color space image is transformed into HSV color space, and color saliency is calculated. Then, the SLI C algorithm is used to construct the superpixel patch structure of the image. In LAB color space, the contrast saliency is calculated. Finally, the salient images of color feature and contrast feature are fused and multi-scale enhanced. Image saliency detection is carried out on MSRA — 1000 data set. Compared with the existing methods, the algorithm in this paper detects more complete contour of image saliency region, has better precision and recall rate, and can obviously suppress complex texture and noise.
Heterogeneous Multi-Robot Path Planning Based on Probabilistic Motion Model

An important problem in multi-robot system is how to coordinate robot’s motion such that each robot can complete its task without collision. Previous approaches assume that robots’ motion is deterministic once their paths have been planned, which is however not realistic because random interferences such as noise, friction and inaccurate control input in real-life system could disturb the robot motion, leading to a stochastic behavior. In this paper, we take this stochastic behavior into account when planning the path for a heterogeneous multi-robot system. We assume that the motion time of a robot from a location to another can be modeled as a probability distribution. Every robot has its own probability distribution of motion time between any two neighbor locations. We develop a conflict-detection scheme for this model and propose using the conflict-based search algorithm via probability calculation to find the optimal path that minimizes the entire motion time. We also simplify this conflict detection such that our proposed approach is applicable online for a large-scale system. Experimental results demonstrate the high effectiveness of our proposed approaches.
Preliminary Investigation of Visual Information Influencing Driver’s Steering Control based on CNN

Understanding the relationship between driving behavior and visual information is an important issue in order to understand driving behavior holistically. In this study, we constructed a driver model that reproduces the driver’s steering behavior from visual information based on the Convolutional Neural Network (CNN) with human physical characteristics. We obtained the driving behavior in a simulator study to train the proposed CNN model. Which region in the visual field influencing drivers’ steering behavior was analyzed using the results of the feature maps generated by the trained CNN model and the driver’s gaze behavior. The results indicate that the drivers perform steering action using the information within 20 degrees from the gaze point.
Study and Practice on Control Strategy of Leader-Follower Manned-Unmanned Aerial Vehicle Formation Based on PIXHAWK Flight Control System*

This article takes the control strategy and practical method of Leader-Follower Manned-Unmanned Aerial Vehicle Formation for research purposes, choose PIXHAWK flight control system and Multi-rotor unmanned aerial vehicle (UAV) for verification platform. It analyzed the dynamic equation, the control algorithm and the process of the Leader-Follower formation flight control system. The platform applied XBeeS3B communication module structures to set up machine communication and realize the data interaction. And on the basis of setting the formation distance between aircraft, four following control strategies including following position, following speed, receiving target position signal and waiting for receiving target position signal were adopted to complete formation flight engineering verification.
A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction

Vehicle-to-vehicle communication is a solution to improve the quality of on-road traveling in terms of throughput, safety, efficiency and comfort. However, road users that do not communicate their planned activities can create dangerous situations, so prediction models are needed to foresee and anticipate their motions in the drivable space. Various prediction methods exist, either physics-based, data-based or hybrids, but they all make conservative assumptions about others’ intentions, or they are developed using unrealistic data, and it is unclear how they perform for trajectory prediction. In this work, we introduce and demonstrate an optimal hybrid framework that overcomes these limitations, by combining the predictions of several physics-based and data-based models. Using on-road measured data we show that this novel framework outperforms the individual models in both longitudinal and lateral position predictions. We also discuss the required prediction boundaries from a safety perspective and estimate the accuracies of the models in relation to automated vehicle functions. The results achieved by this method will enable increased safety, comfort and even more proactive reactions of the automated vehicles.
Virtual reality sickness detection: an approach based on physiological signals and machine learning

Virtual Reality (VR) is spreading to the general public but still has a major issue: VR sickness. To take it into consideration and minimize its occurrence, evaluation methods are required. The current methods are mainly based on subjective measurements and therefore have several drawbacks (e.g., non-continuous, intrusive). Physiological signals combined with Machine Learning (ML) methods seem an interesting approach to go beyond these limits. In this paper, we present a large-scale experimentation (103 participants) where physiological data (cardiac and electrodermal activities) and subjective data (perceived VR sickness) were gathered during 30-minute VR video game sessions. Using ML methods, models were trained to predict VR sickness level (based on the physiological data labeled with the subjective data). Results showed an explained variance up to 75% (in a regression approach) and an accuracy up to 91% (in a classification approach). Despite generalization issues, this method seems promising and valuable for a real time, automatic and continuous evaluation of VR sickness, based on physiological signals and ML models.
Simulation of the Discharge Regime Transition under Repetitive Nanosecond Pulses in Nitrogen at Atmospheric Pressure

In this paper, a two-dimensional particle model coupled with an external electrical circuit is proposed to study the evolution of the streamer dynamics under nanosecond repetitively pulsed discharges. Discharge regime transitions between corona, glow and spark regimes under single and repetitive nanosecond pulses are investigated. The dominating factors, such as charged particles accumulation and relaxation, are taken into consideration to give theoretical explanations.
Replay attack detection using a zonotopic KF and LQ approach

This paper exploits the analogy between the stochastic and zonotopic Kalman filters with the aim of formulating a metric that allows to assess the impact of an external zonotopically bounded signal in an state estimate optimal control scheme. To that end, the concept of linear quadratic zonotopic (LQZ) controller is introduced. Besides, the design of a zonotopically bounded watermarking signal such that enforces replay attack detectability is also addressed. The proposed detection scheme takes advantage of the observability loss of an a priori known exogenous signal during the replay phase of the attack. Consequently, by injecting the corresponding estimation error generated by a set-based observer, a zonotopically bounded signal such that imposes replay attack detectability is achieved. A numerical example is provided.
Towards A Tactical UAV Assessment System to Support Transport Helicopter Crews in MUM-T Scenarios

By nature, military Manned-Unmanned Teaming (MUM-T) missions are characterized by complex state spaces, uncertainty and multiple types/sources of information that the pilot needs to assess in a time critical manner to derive tactical decisions. In order to support the helicopter crew in such missions, we investigate a software agent that interprets available situational and mission related information to create machine situational awareness. In this article, we present a work in progress on the concept and implementation of a tactical situation assessment system using an Influence Map evaluation polytree for knowledge representation. Furthermore, we look at the problem from a human autonomy teaming perspective. The agent provides tactical recommendations to the pilot and shares its inferred knowledge about possible future critical states. We present the current state of integration in our helicopter research flight simulator and outline challenges concerning effective human-machine cooperation in tactical decision-making.
On the representation of coupled adiabatic potential energy surfaces using quasi-diabatic Hamiltonians: description of accidental seams of conical intersection

The ability to describe seams of conical intersection that couple adiabatic potential energy surfaces is a key issue in describing nonadiabatic processes. This work considers the description of these seams provided by a recently introduced description of coupled adiabatic states using a coupled quasi-diabatic state Hamiltonian, H d, whose matrix elements are polynomials in the internal coordinates. The conical intersection seam is represented in intersection adapted coordinates. Using the seam of accidental conical intersection of the 1,21A states of NH3 as an example the ability of the proposed algorithm to describe a seam of conical intersection in its full dimensionality is demonstrated. The success of this description is attributable to the use of orthogonal intersection adapted coordinates and to the Lagrange multiplier constraints in our algorithm which enables us to require the locus, energies and local topography of selected points on the H d determined seam of intersection to agree exactly with the ab initio results.