Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Recursive Framework for Trajectory Generation of UAVs

Unmanned aerial vehicles (UAVs) have recently gained increasing attention. Self-positioning and integrated navigation are main aspects during a flight mission and rely on predefined trajectories. Current guidance and control systems provide real-time control laws to track desired trajectories given by the Embedded Flight Management System (E-FMS). Since some UAVs are highly non-linear systems with under-actuation properties from the control point of view, discontinuities in control inputs (coming from trajectories generated by E-FMS) can produce undesired vibrations causing aging and damages in the structure. In this paper, we propose a Recursive Smooth Trajectory generation algorithm (RST) that allows for finding a smooth $\mathcal{C}^{\propto}$ polynomial path, and thus a close form trajectory satisfying any arbitrary dynamic limitations translated into kinematic constraints (e.g. position, velocity, acceleration, etc). Each kinematic constraint is recursively fulfilled, leading to a fast online implementation for the E-FMS. The storage requirements and execution time are therefore discussed in this paper. The effectiveness and suitability of the RST algorithm over a minimum-snap piecewise polynomial approach for highly nonlinear UAV flight is also analyzed.
Side-scan Sonar Image Rough Recognition and Feature Matching Based on CNN and SIFT

Compared with traditional single-beam side-scan sonar device, current side-scan sonar have greatly improved the stability, resolution and definition of sonar image, which leads to a large increase in the amount of data, so it is impossible to completely identify target artificially. During the side-scanning sonar operation, sonar equipment is affected by ocean currents, wind waves, etc., resulting in posture instability and image distortion. The complex underwater environment and the interference of its own will also cause a lot of noise in the collected sonar image. The problems above have brought great difficulties for target recognition. This paper utilizes LeNet5 convolutional neural network (CNN) to perform automatic rough recognition of side-scan sonar images, and then uses scale-invariant feature transform (SIFT) feature matching for further recognition. The processing of experimental data shows that the accuracy and efficiency of target recognition are good using these two algorithms.
Ancient Chinese Character Image Segmentation Based on Interval-Valued Hesitant Fuzzy Set

To address the low segmentation accuracy caused by the rich glyph styles of ancient Chinese characters and the complex layout of ancient Chinese books, which affects the retrieval and recognition results, an algorithm for the layout image analysis of ancient Chinese books and Chinese character image segmentation is proposed. The initial segmentation results were obtained through the projection method of the layout of ancient Chinese books, and the connected component analysis of the above results was carried out to determine the rough divided blocks of under-segmentation and over-segmentation. Considering under-segmentation of adhesive Chinese characters, the improved K-means clustering method was used to segment adhesive blocks to obtain single-character images. To address the over-segmentation of character components separation, a method based on interval-valued hesitant fuzzy set is proposed. This method analyzed the features of the connected component in the block, characterized the over-segmentation connected component. The hesitant fuzzy distances between other connected components and the standard merge evaluation interval number were calculated in sequence. The connected component with the smallest distance was preferentially merged with the over-segmentation connected component until no over-segmentation connected component remained in the block. The experimental segmentation accuracy was 89.94%.
Species richness and seed dispersal to secondary woods in southern Poland

Twenty-seven secondary woods, 32-51 years old, occurring on a limestone hill near Krakow, Poland, were investigated. Twenty-five woods were Scot's pine woods; twelve of them were planted. They differ in area, shape and isolation from the primary woods. 195 species of vascular plants were found in the secondary woods, including fifty-nine small shrub and herb woodland species (out of the seventy woodland species growing in the nearby primary woodlands). Only thirteen woodland species occuffed in more than a half of the secondary woods. Statistical analysis included ten independent variables (area, age, shape, distance to primary woodlands, cover of tree and shrub layer, and others) and two dummy variables. The results of a stepwise multiple regression analysis show that the number of all species depends only on area. The number of woodland species is significantly dependent on distance from the source of diaspores, area, shape, age and type of soil. The number of conifer woodland species is independent of wood age, which affects the number of broadleaf woodland species. A close relationship was found between the type of dispersal of woodland species and their distribution in secondary woods. Hovering and flying anemochores and endozoochores are the best colonizers, the worst are heavy anemochores, myrmecochores and barochores. The slow rate of the secondary succession in the study woods is due to poor colonizing ability of many woodland species as well as to the effect of the xeric conditions on the establishment and growth of seedlings.
Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Electronic health records are used to extract patient’s information instantly and remotely, which can help to keep track of patients’ due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using ‘HIPAA Safe Harbor’ technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.
Transceiver Architectures for Fully Integrated Frequency Division Duplex (FDD) Systems with Wideband TX Interference Cancellation

In this paper, we present an overview of the challenges in frequency division duplex (FDD) integrated transceivers and propose new architectural concepts that can meet demanding FDD performance requirements and be fully integrated on-chip. System-level simulations using TSMC's 65 nm bulk CMOS technology confirm that the proposed architectures can operate in FDD mode for TX powers higher than 20 dBm with lower than 1 dB degradation in TX and RX insertion losses over a frequency range wider than 1 GHz under antenna mismatches and high TX noise floor.
Kontur-3: Human Machine Interfaces for Telenavigation and Manipulation of Robots from ISS

Space agencies are planning crewed lunar and Mars exploration missions to be realized within the next decade [1]. Sending humans directly to the surface of the celestial bodies is, however, extremely dangerous and costly. Therefore, humans will teleoperate robots from an orbital spacecraft to explore the surface, acquire samples and to construct habitats. In the German-Russian Kontur-2 space project (2012–2017), the basic telerobotic scenario was realized, i.e. cosmonauts located in an orbiter (ISS) controlled robotic systems on a planet (Earth) with a force feedback joystick. This paper presents the planned experiments and Human Machine Interface (HMI) devices for the Kontur-3 space project, which is a follow-up project of Kontur-2. The paper reports the main technical achievements and human factors results of the Kontur-2 project and, the need for more effective HMI devices to be developed in Kontur-3. The preparations for the ISS experiments with the newly developed devices would bring new insights to the research community and can also become a major contribution to the planning, development and implementation of future space missions.
Grid-Connected PV System Using a T-Type qZS Inverter with an Integral Time Derivative Approach to Ensure MPP and Decoupled Current Control

Three-level quasi-Z-Source (qZS) inverters based on the T-Type topology are especially indicated to be used in grid-connected PV systems. In fact, they present an important feature for this type of application since presents Buck-Boost characteristic. Besides that, is also characterized by high reliability and multilevel operation. However, associated to this converter, it must be used controllers that will ensure their best performance. In this context, this paper will propose a global control system for the three phase T-Type qZS inverter in a grid-connected PV System. So, it will be considered a MPPT algorithm based on a robust integral time derivative approach. For the output currents will be used a decoupled current controller in which will be associated to a SPWM modulator. The performance of the system will be verified and tested by simulation studies. The results will show that this system will provide results well adapted for this application.
Small targets recognition in SAR ship image based on improved SSD

Synthetic Aperture Radar(SAR) ship recognition is significant in marine applications and plays an important role in maritime traffic management, fisheries management, and maritime rescue, etc. A major difficulty in SAR ship recognition is that the SAR ships have a small size in images, which results in low recognition accuracy of the SSD. This paper first analyzes the reasons for the low recognition accuracy for small targets in SSD. First, the poor matching of the default boxes leads to a small number of positive samples. Second, the representation ability of low-level feature map for recognizing small targets is weak. Then two strategies are proposed to improve the SSD. Firstly, a default box optimization design method based on Kmeans clustering is proposed, which improves the matching performance of the default boxes. Secondly, a feature fusion method based on deconvolution is proposed, which effectively improves the representation ability of low-level feature maps. The experimental results show that the proposed method can greatly improve the recognition accuracy of SSD for small targets in SAR ship images.
The Artemis Program: An Overview of NASA's Activities to Return Humans to the Moon

NASA is developing a two-phased approach to quickly return humans to the Moon and establish a sustainable presence in orbit and on the surface. The two phases run in parallel, and both have already begun, with selection of the first Gateway element, the Power and Propulsion Element, solicitation activities focused on an American-built, industry-provided Human Landing System, and discussions with industry and international partners about potential opportunities for collaboration. Phase 1 is driven exclusively by the administration's priority to land the first woman and the next man on the lunar South Pole by 2024. In this phase, NASA and its partners will develop and deploy two Gateway components: the Power and Propulsion Element (PPE) that will launch in 2022, and the Habitation and Logistics Outpost (HALO), a minimal habitation capability) that will launch in 2023. Both will launch on commercial rockets, as will Gateway logistics deliveries to outfit the ship and provide supplies for surface expeditions. This initial Gateway configuration represents the beginning of its capability buildup, and the primary components required to support the first human expedition to the lunar South Pole. NASA's baseline reference approach for human expeditions on the surface is for Human Landing Systems to aggregate and dock to the Gateway, then deploy to the lunar South Pole with two astronauts aboard. Phase 2 is focused on advancing the technologies that will foster a sustainable presence on and around the Moon - a lasting and productive presence enabled by reusable systems, access for a diverse body of contributing partners, and repeatable trips to multiple destinations across the lunar surface. In this Phase, we will advance sustainable systems to make surface expeditions more repeatable and affordable. While the Gateway is the first of its kind to be funded, the concept has been proposed for decades as a necessary and foundational capability for a sustainable return to the Moon, and a port for vehicles embarking to farther destinations. It supports every tenet of Space Policy Directive-1 and the infrastructure it provides is critical to an accelerated return to the Moon, and access to more parts of the Moon than ever before. The Gateway also provides a unique platform to conduct cross-discipline science. Science instruments, both internal and external to the Gateway, have the potential to reveal new findings in space science, Earth science, and biological research data from deep space. Additionally, the broad science community will be able to utilize the communications and data relay capabilities of the Gateway, beginning with the PPE in Phase 1. This paper will outline the cross-discipline activities NASA is currently conducting, and those the agency anticipates conducting in the future to successfully implement Phases 1 and 2 in the lunar vicinity, all while preparing for humanity's next giant leap: Mars.
A Second-Order Method for Fitting the Canonical Polyadic Decomposition With Non-Least-Squares Cost

The canonical polyadic decomposition (CPD) can be used to extract meaningful components from a tensor. Most existing optimization methods for fitting the CPD use as cost function the least-squares distance between the tensor and its CPD. While the minimum of this cost function coincides with the maximum likelihood estimator for data with additive i.i. d. Gaussian distributed noise, for other noise distributions, better-suited cost functions exist. For such cost functions, first-order, gradient-based optimization methods have been proposed. However, (approximate) second-order methods, which additionally use information from the Hessian of the cost function to achieve faster convergence, are still largely unexplored. In this paper, we generalize the Gauss–Newton nonlinear least-squares algorithm to twice differentiable entry-wise cost functions. The low-rank structure of the problem is exploited to keep the computational cost low. As a special case, $\beta$-divergence cost functions are examined. We show that quadratic convergence can be obtained close to the solution with a reasonable extra cost in memory and computation time, making the proposed method particularly useful when high accuracy of the decomposition is desired.
Rusty: Runtime Interference-Aware Predictive Monitoring for Modern Multi-Tenant Systems

Modern micro-service and container-based cloud-native applications have leveraged multi-tenancy as a first class system design concern. The increasing number of co-located services/workloads into server facilities stresses resource availability and system capability in an unconventional and unpredictable manner. To efficiently manage resources in such dynamic environments, run-time observability and forecasting are required to capture workload sensitivities under differing interference effects, according to applied co-location scenarios. While several research efforts have emerged on interference-aware performance modelling, they are usually applied at a very coarse-grained manner e.g., estimating the overall performance degradation of an application, thus failing to effectively quantify, predict or provide educated insights on the impact of continuous runtime interference on per-resource allocations. In this paper, we present Rusty, a predictive monitoring system that leverages the power of Long Short-Term Memory networks to enable fast and accurate runtime forecasting of key performance metrics and resource stresses of cloud-native applications under interference. We evaluate Rusty under a diverse set of interference scenarios for a plethora of representative cloud workloads, showing that Rusty i) achieves extremely high prediction accuracy, average <inline-formula><tex-math notation="LaTeX">$R^2$</tex-math><alternatives><mml:math><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="masouros-ieq1-3013948.gif"/></alternatives></inline-formula> value of 0.98, ii) enables very deep prediction horizons retaining high accuracy, e.g., <inline-formula><tex-math notation="LaTeX">$R^2$</tex-math><alternatives><mml:math><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="masouros-ieq2-3013948.gif"/></alternatives></inline-formula> of around 0.99 for a horizon of 1 sec ahead and around 0.94 for an horizon of 5 sec ahead, while iii) satisfying, at the same time, the strict latency constraints required to make Rusty practical for continuous predictive monitoring at runtime.
A Simple Method to Realize Millilens Array on Encapsulant Layer for Enhancing Light Efficiency of COB-LEDs

The chip-on-board light-emitting diodes (COB-LEDs) present low light efficiency due to the total internal reflection (TIR) at the flat encapsulant–air interface. In this study, a simple method based on complementary printing was proposed to realize the millilens array on the flat encapsulant layer to diminish the TIR effect, and thereby improve the light efficiency of the COB-LEDs. Experiments and optical simulation based on the Monte Carlo ray-tracing method were conducted to investigate the effect of the lens curvature <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, lens radius <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>, and lens spacing <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> on the light efficiency of the COB-LEDs. The results show that the light efficiency of the COB-LEDs increases with <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> and decreases with <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>, and COB-LEDs with hemispherical lens arrays (<inline-formula> <tex-math notation="LaTeX">${\theta = }90^{\circ }$ </tex-math></inline-formula>) have the highest light efficiency when <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> are fixed. Compared with the conventional COB-LEDs with a flat encapsulant, closely arranged hemispherical lens arrays with an <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> of 0.5, 1, and 1.5 mm enhance the light efficiency of the blue COB-LEDs by 87.56%, 90.62%, and 95.73% and enhance the light efficiency of ~6000-K white COB-LEDs by 17.24%, 18.86%, and 20.33%, respectively.
Anobeat: Anomaly Detection for Electrocardiography Beat Signals

Electrocardiography signals are composed of variform heartbeats which could indicate the condition of the heart and reveal the risk of heart attacks. Many existing classification-based works for abnormal beats detection are limited by the class-imbalanced data or labor-intensive manual annotation bias. A promising trend to address the issue is to identify the abnormal data that differs from the normal data by utilizing normal (oneclass) data to learn the manifold and detect the anomaly to the unseen and unlabeled data in an/aunsupervised/semi-supervised manner. In this paper, we propose Anobeat, a semi-supervised approach, to perform the abnormal beat detection by facilitating adversarial regularized autoencoders constrained with multifeature and reconstruction error. In order to obtain a robust and reasonable latent coding, we deploy two discriminators in the latent space and visual space to distinguish real and fake features and minimize the distance between two features to train the visual discriminator in alternate steps. Meanwhile, we minimize the reconstruction error and maximum distance between input and noise features to improve the decoder. The adversarial multi-feature constraints enable the generator to learn the latent representations of the target normal data and reconstruct the beats properly. Experiments showed that Anobeat achieved ROC-AUC of 0.960 and 0.894 in the MIT-BIH intrapatient and inter-patient dataset respectively, which outperforms the most competitive baseline by 1.61% and 0.62% respectively. Anobeat also performs comparative robustness and shows good interpretability in the European ST-T and MIT-BIH Arrhythmia Database.
Immunohistochemical Localization of Met-enkephalin in Mantles and Feet of Octopus Octopus ocellatus Gray

Met-enkephalin in mantles and feet of octopus(Octopus ocellatus Gray) was examined and analyzed by immunohistochemical SABC technique.The results showed that there was Met-enkephalin in the dorsal and ventral mantles,wrists, wrist membrane,and funnels,especially in the mantles and feet.The strong positive Met-enkephalin immunoreaction was observed in the epithelia in the dorsal mantle,wrist,membrane of the octopus and the epidermis of the ventral mantle,and funnels.It can be inferred that the various density distribution of the Met-enkephalin in the epidermis of the mantle and feet is attributed to the different functions.