Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Broadband Quasi-Zero-Refractive-Index Metamaterials With Modified Fishnet Structure

Metamaterials have the potential to create meta-devices with new and diverse properities based on novel wave phenomena. Due to the strong inherent resonances, most meta-materials are inherently dispersive, which limits operational bandwidths and leads to high absorption losses. Here, we theoretically present a broadband zero-index-metamaterial (ZIM) with near-zero permittivity and near-zero permeability. The ZIM is realized by a modified fishnet structure using full-wave finite-element simulation. Numerical results illustrate that in the $30.7\sim 37.7\mathrm{GHz}$ band, the average transmitted power at normal incidence is above 94% (~0.7 dB). With the retrieval procedure by using S parameters obtained from numerical simulations, we illustrate that in the $31.80\sim 35.49\mathrm{GHz}$ band, the refractive index less than $1(\vert \mathrm{n}\vert < 1)$.
Inshore Ship Detection Based on Convolutional Neural Network in Optical Satellite Images

In this paper, we present a novel inshore ship detection method based on convolutional neural network (CNN). Different from current inshore ship detection methods that need complex shape and texture analysis or sea and land segmentation, our method starts from a global search for the relatively distinct ship head with an efficient classification network. This can help to obtain the location of possible ship heads as well as the rough ship directions, which are beneficial to generate smaller and more precise candidate regions of ship targets. Compared with other region proposal methods, our method can produce a rather smaller set of proposals. Next, iterative bounding-box regression and classification are unified into a multitask network, which is constructed and trained specially by considering the practical condition of the inshore ships in remote sensing images. At last, nonmaximum suppression is applied to eliminate duplicate detections. Experiments on optical satellite images demonstrate the effectiveness and robustness of the proposed method for inshore ship detection.
Detection for Pulmonary Nodules using RGB Channel Superposition Method in Deep Learning Framework

The detection of pulmonary nodules is a very important research field in computer-aided diagnosis. In order to help doctors to identify pulmonary nodules more conveniently, especially for some small pulmonary nodules, a method based on RGB channel superposition to detect pulmonary nodules is proposed in this paper. We put the same ROI (region of interest) from three sequential lung CT slices into RGB channels to gain a pseudo-color image for deep learning. AlexNet and GoogLeNet is used as the deep learning network. We use 10000 patches of healthy tissues and 12000 patches of pulmonary nodules in LIDC-IDRI dataset for training and get a prediction model. The model is tested on 176 patients’ CT images and gain the sensitive of 95.0% at 5.62 false positives per scan. The experimental results show that the proposed method can improve the detection rate of pulmonary nodules compared with some traditional feature extraction methods.
Stability Analysis and Hopf Bifurcation Research for DNA Strand Displacement with Time Delay

In this paper, a nonlinear system with time delay mode for DNA strand displacement based on toehold exchange firstly is proposed. The boundedness of all the state variables of the DNA strand displacement system is proved. After that, local stability analysis is performed around the positive equilibrium point to obtain a necessary and sufficient condition for local stability, which depend on the coefficients of the characteristic equation corresponding to linearization of the system. In addition, the existence of the Hopf bifurcation is analyzed, which shows that the time delay parameter will guarantee that the system is stable within a certain range. However, Hopf bifurcation and chaos occur when the time delay parameter increases continuously and even exceeds the critical value. At last, numerical simulations have verified the correctness and validity of the DNA strand displacement studies.
Automated Interpretation of Seafloor Visual Maps Obtained Using Underwater Robots

Scientific surveys using underwater robots can recover a huge volume of seafloor imagery. For mapping applications, these images can be packaged into vast, seamless and georeferenced seafloor visual reconstructions in a routine way, however interpreting this data to extract useful quantitative information typically relies on the manual effort of expert human annotators. This process is often slow and is a bottleneck in the flow of information. This work explores the feasibility of using Machine Learning tools, specifically Convolutional Neural Networks (CNNs) to at least partially automate the annotation process. A CNN was constructed to identify Shinkaia Crosnieri galetheid crabs and Bathymodiolus mussels, which are two distinct megabenthic taxa found in vast numbers in hydrothermally active regions of the seafloor. The CNN was trained with varying numbers of annotated data, where each annotation consisted of a small region surrounding a positive label at the centre of each individual within a seamless seafloor image reconstruction. The performance was assessed using an independent set of annotated data, taken from a separate reconstruction located approximately 500 m away. While the results show that the trained network can be used to classify new datasets at well characterized levels of uncertainty, the performance was found to vary between the different taxa and with a control dataset that showed only unpopulated regions of the seafloor. The analysis suggests that the number of training examples required to achieve a given level of accuracy is subject dependent, and this should be considered by humans when devising annotation strategies that make best use of their efforts to leverage the advantages offered by CNNs.
Online Updating for Gaussian Process Learning

Gaussian processes for regression and classification have become an effective machine learning methodology with a number of distinctive advantages. One notable disadvantage of Gaussian process methods is the computational complexity related to the inversion of matrices, especially for applications that involve large datasets. In this paper, an exact online updating algorithm is presented to significantly reduce the amount of computations for repeated progressive trainings of Gaussian processes.
International Conference on Intelligent and Advanced Systems 2007 Mesh Architecture for Hardware Implementation of Particle Swarm Optimization

—Particle Swarm Optimization (PSO) is an evolutionary computation method which has successfully been used in many engineering optimization problems. The major obstacle limiting the use of PSO in real-time applications is its long execution time. Hardware implementation of evolutionary algorithms has been employed to alleviate the high computational cost of complex optimization problems. In this paper, we propose a parallel scalable architecture which is well-suited for hardware implementation of PSO. The architecture is composed of a number of Processing Elements (PE) performing the algorithm computations that are connected to other PEs through communication channels. PEs are arranged based on the mesh architecture which provides either scalability or performance to execute computational intensive applications. Two communication methods are proposed based on the architecture which enable the system to solve different kinds of problems. I. INTRODUCTION Particle Swarm Optimization (PSO) [1], [2] is one of the evolutionary computation techniques based on swarm intelligence. Like the other evolutionary computation techniques, PSO is a stochastic population-based search algorithm. In PSO, a potential solution, called a particle, represents a point in the search space which has a fitness value and a velocity. Each Particle flies through the solution space of the problem and adjusts its flying velocity to search for the global optimum according to its own and social historical experiences. Therefore, the particles have a tendency to fly towards the better and better search area over the course of search process.
Draft Genome Sequence of the Plant Growth-Promoting Rhizobacterium Pseudomonas protegens Strain BNJ-SS-45, Isolated from Rhizosphere Soil of Wheat (Triticum aestivum)

Here, we present the draft genome sequence of Pseudomonas protegens strain BNJ-SS-45, which was isolated from wheat rhizosphere. The genome is assembled with 7,116,445 bp with a GC content of 63.34% consisting of 32 scaffolds. ABSTRACT Here, we present the draft genome sequence of Pseudomonas protegens strain BNJ-SS-45, which was isolated from wheat rhizosphere. The genome is assembled with 7,116,445 bp with a GC content of 63.34% consisting of 32 scaffolds. The genome is useful in prediction of secondary metabolites, particularly rhizoxin, pyoverdine, and bacteriocin.
Robust Adaptive Extended Kalman Filtering for Smart Phone-based Pedestrian Dead Reckoning Systems

The expansion of Location-based services and applications leads to extensive interests on smart phone-based indoor and outdoor localization. Rich sensors embedded in smart phone support varies of localization techniques, provide infrastructural elements for indoor and outdoor seamless localization solutions. The pedestrian dead reckoning (PDR) system based on smart phone-embedded MEMS sensors plays an important role in a seamless localization system, since it can link up different absolute positioning systems (such as BeiDou Navigation Satellite System (BDS), WiFi localization systems, etc.) flexibly. However, as a relative localization system, it is limited to location error accumulation, and therefore it cannot run for long. The problem can also affect the performance of a seamless localization system. As a result, in order to improve the tracking performance of the PDR system in complex environments indoors and outdoors, a method based on Robust Adaptive Extended Kalman Filtering (RAEKF) is proposed. The method includes heading and speed estimation, for heading estimation, outputs from gyroscope, accelerometer, and magnetometer sensors are used, and for speed estimation, only outputs from accelerometer are used. RAEKF is employed both in heading and location estimation. Although speed and location estimation refer to different state and measuring models, the proposed filtering can be applied flexibly. The M-estimator is used to handle measurement outliers. To weaken the impacts of dynamic disturbance errors for heading and location estimation, an adaptive factor is introduced to adjust their models respectively. Extensive experiments on static and dynamic localization were conducted in indoor complex environments. And the experimental results demonstrate the proposed method provides more accurate and robust performances, compared with methods based on conventional EKF.
Effects of Leading-Edge Tubercles on Hydrodynamic Characteristics at Different Reynolds Numbers

Recently the leading edge tubercles which are believed to significantly help the whale to execute rolls and loops under water have attracted great attention as one of the passive flow control methods. Many researchers have studied the effects of the convex structure of a humpback whale on the aerodynamic performance of the blade. However, the effect of leading edge tubercles on hydrodynamic characteristics, especially in the turbulent regime, is still being investigated. The purpose of this paper is to investigate Reynolds number effects on the hydrodynamic characteristics induced by a hydrofoil with leading edge tubercles in transitional and near-turbulent regimes using computational fluid dynamic. The Reynolds numbers based on chord of transition and near-turbulent regimes are respectively 160,000 and 1,000,000. It is found that the tubercled hydrofoil has a spanwise pressure gradient at the leading edge, and the pressure gradient between the peak and trough drives the fluid to the low pressure region. Three-dimensional laminar separation bubbles (LSBs) are formed after the fluid flow over the hydrofoil. As the angle of attack increases, the separation bubbles move forward. Under small angles of attack, the pressures, spanwise velocity and normal velocity at the two Reynolds numbers are similar in the distribution, but the pressure and velocity are very different under large angles of attack. In addition, the position and shape of the LSBs are different at the two Reynolds numbers. The range of LSBs is larger at low Reynolds number, because complete turbulence at high Reynolds number inhibits flow separation. The numerical simulation results show that the influence of the leading edge tubercles on the lift and drag of the foil is different under different Reynolds numbers.
The use of simulated annealing to solve the mobile manipulator path planning problem

The planning problem is considered for a mobile manipulator system which must perform a sequence of tasks defined by position, orientation, force, and moment vectors at the end effector. Each task can be performed in multiple configurations due to the redundancy introduced by mobility. The planning problem is formulated as an optimization problem in which the decision variables for mobility (base position) are separated from the manipulator joint angles in the cost function. The resulting numerical problem is nonlinear with nonconvex, unconnected feasible regions in the decision space. Simulated annealing is proposed as a general solution method for obtaining near-optimal results. The problem formulation and numerical solution by simulated annealing are illustrated for a positioning system with five degrees of freedom. These results are compared with results obtained by conventional nonlinear programming techniques customized for the particular example system. <<ETX>>
Docking Experiment in Dark Environments Using Active/Lighting Marker and HSV Correlation

Recharging ability with underwater docking function would be a first primal step conducted to enable the AUV to operate independently of a surface vessel for extended periods. Therefore, the role of docking operation came in picture not only for battery recharging application but also other novel applications such as sleeping under mother ship, or new mission up and down loading. Moreover, docking capacity can be extended to provide navigation for other underwater vehicles on the way of their mission too. However, there are many challenging issues in achieving these applications that request high accuracy and robustness against disturbances that are provided by the underwater environment. The most challenging and unavoidable problems in sensing sphere for sea operations are, we think, turbidity and light changing. Turbidity is defined as cloudiness in a liquid caused by the presence of suspended particles that scatter and absorb light. Since underwater battery recharging are supposed as a first step to realize a full autonomous/intelligent robot, the deep-sea docking experiments cannot avoid turbidity and low light environment. In previous studies, we had conducted sea docking experiments using a passive (not lighting) marker and image-evaluation function based on only hue information, limiting its operational environment in lower turbid sea with sunshine. Whereas in this study, to improve our system removing above defects, we newly designed an active - light emitting - 3D marker and a fitness function determined by HSV color components to improve the performance of the system especially in a more turbid environment. The advantage of using an active 3D marker and HSV-evaluated function is to be thought as being tolerable and seeable despite clipped whites and scattered light on the camera images caused by turbidity. Additionally, we conducted the docking experiments to verify the robustness of the proposed approach against turbidity and compared recognition results between the previous method and the improved method.
Emerging Terahertz Technologies for Security, Quality Control, Vision and Medical Applications

The spectrum of electromagnetic radiation covers a broad range of frequencies, spanning from micro-, millimeterand radiowaves from one side to visible, ultraviolet and X-ray bands on the other. Nowadays, both these spectral ends are effectively explored in a multitude of devices and applications. In contrast, the intermediate zone, grouping electromagnetic waves of frequencies from 100 GHz to 10 THz, remains largely unexplored (Fig. 1),
Motion planning with time-varying polyhedral obstacles based on graph search and mathematical programming

A method is presented for path planning of a point robot moving among polyhedral obstacles with piecewise constant translating velocity. Representing the obstacles as polyhedra in the space-time domain allows a collision-free, minimum-time path to be obtained using graph search and linear programming. The planner views the space-time configuration of free space as disjoint polytopes where each point in the space-time domain is mapped into a unique polytope. The planner searches connected polytopes between the start and goal polytopes that satisfy the speed and time constraints. A near-optimal trajectory is determined by constrained optimization. In the proposed approach, the obstacle is allowed to move faster than the point robot, and the methodology can be easily extended to motion planning in higher-dimensional spaces. <<ETX>>
Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter

In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods.
Event-Driven Production Rescheduling in Job Shop Environments

Unpredictable incoming orders and the required nesting process highly complicate production planning and scheduling in sheet metal job shop environments and cause extremely high lead times as well as intermediate stocks. For this, numerous advanced planning and scheduling (APS) algorithms exist, aiming at creating a globally optimized production schedule. Due to the complexity of the multi-objective optimization and the large amount of unforeseen shop-floor events, effective and applicable solutions have not been presented so far. This work introduces an event-driven rescheduling concept based on lean principles leading to a high responsiveness of the production process to any kind of deviation. The achieved, significantly smaller buffer occupancies enable shorter lead times and improved delivery time estimations. Excellent performance results of the rescheduling concept are shown in different simulation experiments. The presented concept can easily be implemented in any kind of sheet metal job shop and its respective IT infrastructure.
CSI-based Indoor Tracking with Positioning-Assisted

High accurate indoor positioning and tracking is crucial to pervasive applications such as elderly care, intrusion monitor and behavior analysis. Recently, Channel State Information(CSI) as a fine grained physical layer information has been proposed to achieve high positioning accuracy by using laborious learning-based methods. In this work, we propose a CSI-based indoor tracking system which combines velocity estimation and dead reckoning to track human trajectory. Additionally, an efficient quantization method for depicting the correlation between human velocity and CSI dynamics is raised. On this basis, the results of indoor positioning could be used for trajectory initialization and calibration. Extensive experiments demonstrate that our system provides high accurate trajectory information.
SENTIŌ: Reconnect with Yourself to Better Connect with Others

Occidental societies are becoming increasingly demanding. Citizens often feel overwhelmed and feel the need to rest, which they do so by isolating themselves from others. Individuals who use this time alone to reflect on their situation find that this reflection helps create deeper connections with others afterwards. With this in mind, we developed Sentiō, a solution that aims to encourage people to take time for themselves in hopes that it will lead them to be more open-minded when interacting with others. The solution helps people focus on bodily sensations and generates a comfortable and effortless time for introspection. Sentiō uses the subconscious benefits of perceiving one's heartbeat to foster personal reflection.
Communication-Less Control of Two-Stage Photovoltaic System with Multiple Distributed Dual-Input Central Capacitor Converters

When multiple distributed dc-dc converters are connected to the single rear-end inverter in the multi-string PV system, the communication between front-end converters and rear-end inverter is required for timely responding to the grid variation in tradition, which however to some extent will limit such application due to the communication reliability and cost. This paper therefore proposes a communication-less control scheme for this kind of two-stage PV system by properly assuming the voltage droop principle to regulate the generated real power. Doing so, the two-stage PV system with multiple distributed dc-dc converters can work well under both steady states and transient states. Matlab simulations can verify the effectiveness of the proposed control strategy.
Optimal Design and Experimental Prototype Testing of a Low-Cost Machine for City Battery Electric Vehicle

This paper presents the optimal design and experimental prototype testing of a low-cost motor applied for a city battery electric vehicle (BEV), zero emission A-segment. Respecting the car specification performances, the aluminum cage rotor induction machine (IM) is designed to reduce motor cost using totally enclosed fan-cooled (TEFC) technology and commercial speed encoders of internal combustion engine (ICE) car. Optimization approach and finite elements analysis (FEA) modeling validation, coupling with thermal calculations are used to size the thermo-electromagnetic parts. After selecting suppliers for motor components, prototypes are manufactured. For the experimentation, first, the indirected flux oriented flux control model is built based on simulation in Matlab/Simulink environment. Then the motor control calibration is tuned on the prototype in test-bench in order to ensure the optimum energy consumption, the current and speed regulations in the entire large operating area. We finally show the experimental prototype testing results to validate the obtained design solution in terms of peak performances, efficiency, thermal and NVH behaviors.