Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Ground Effect for Small-Scale UAVs in Forward Flight

The letter investigates how the behavior of small-scale unmanned aerial vehicles (UAVs) is influenced by the system's close proximity to the ground/rigid surfaces both at hover and in forward flight. We perform an extensive experimental study where a quadrotor is tasked with flying in forward velocities in the range of 0–8 m/s and at altitudes that range between 0.05–0.5 m. Experimental data are used to evaluate four existing ground effect models. Results suggest that existing models for helicopters and in-hover multi-rotors cannot fully describe the forward motion of a quadrotor when it operates close to ground. We introduce two new data-driven models for rotorcraft operating in ground effect both at hover and in forward flight, and evaluate the proposed models with another quadrotor of different size. The proposed models simultaneously consider several operating conditions, which are parameterized by the vehicle's forward velocity and altitude. The models link the thrust produced when operating in ground effect and hovering out of ground effect as forward velocities vary. This information can be incorporated into flight controllers for robust and adaptive UAV flights, and can benefit motion planners for safe and energy efficient near-ground trajectory planning.
Efficiency Analysis of Three-Phase and Dual Three-Phase Induction Machine with Sinusoidal Supply

An efficiency analysis of a three-phase and a dual three-phase machine is juxtaposed in this paper. Therefore, a small-scale prototype induction machine is redesigned for multiphase operation and equal terminal voltage in order to fully exploit the benefit of power segmentation in terms of per phase current rating for high power applications. Sinusoidal supply is used to exclude the influence of time-harmonics due to inverter supply, and enable the separation of losses according to the standard IEC 60032–2. The three-phase winding of the test machine is removed after the measurement, and replaced by the dual-three phase configuration to provide equal magnetic and mechanical conditions. The approach to determine the individual losses according to the standard is modified for multiple three-phase machine. This enables a comparison of the individual loss proportion. The measurements on both machines are evaluated taking into account the standard uncertainties in measurement according to the Guide to the Expression of Uncertainty in Measurement GUM.
PalmGAN for Cross-Domain Palmprint Recognition

Nowadays, many efficient palmprint recognition algorithms have emerged. However, previous algorithms can only be used in a single domain. Furthermore, they also require a large amount of labeled data, which is difficult and costly to obtain. In order to solve these problems, we proposed PalmGAN for cross-domain palmprint recognition. Firstly, the labeled fake images were generated to reduce domain gaps, whose styles are similar to the target domain, and at the same time, the identity information remains unchanged. Based on these fake images, supervised Deep Hash Network (DHN) can be trained and directly used for unsupervised identification in the target domain. Moreover, we established semi-uncontrolled and uncontrolled databases, which were collected in uncontrolled environments. Experiments on several popular databases and self-built databases obtained satisfactory performances. PalmGAN can effectively achieve up to 5.08% improvement for cross-domain recognition, and Equal Error Rate (EER) can decrease to 0% for cross-domain recognition between Blue and Green databases.
A Brief Review of the Intelligent Algorithm for Traveling Salesman Problem in UAV Route Planning

In the future, unmanned aerial vehicles(UAVs) will be used in a wide range of applications in areas such as wireless communications, logistics, inspections, search and rescue. Path planning in these areas often involves Traveling Salesman Problem (TSP). It is extremely difficult to select the optimal path from a large number of nodes, but it is easy to solve the complex problem after converting it to TSP. The optimization methods for solving TSP include meta heuristic algorithm and fuzzy neural network. With the rapid growth of demand, there is an urgent need for faster and more accurate methods to make UAVs more intelligent. This article reviews the application of UAV route planning in the field of wireless communications and express delivery, and selects a variety of classic and up-to-date algorithms for solving TSP and its variants to promote future researches.
Off-line Handwritten Korean Letter using Principle Component Analysis and Back Propagation Neural Network

This paper describes a proposed algorithm for recognition of Korean Letters to the Latin language using Principle Component Analysis (PCA) and Back Propagation-Neural Network (BP-NN). The proposed algorithm uses input in the form of image of Korean letters in original 65×65 pixels that is taken from itself. Then, it will be done some processes namely, pre-processing converts image pixel into binary image 15×15 pixels. Further, it transforms from image Red Green Blue (RGB) into binary. Lastly, noise removal from the image. The image will be extracted to produce the image feature. The feature should be processed firstly using Principle Components Analysis (PCA). PCA is used to reduce dimension of image feature before entering classification stage. Classification stage uses a method that called BP-NN. Architecture of ANN uses three hidden layers. Each layer consists of 20, 20 and 5 neurons, and 1 neuron output. The proposed algorithm uses data sampling that is Korean vowels, are obtained from 25 different font types. Next, each font consists of normal sampling and bold sampling. Total data reaches 500 sampling. The data comprises 70% data training and 30% data testing. The result of experiments show that accuracy level is 95%.
Reduction Methods Using Canceling Effect for Cogging Torque in Dual-Stator PM Synchronous Machines

This paper investigates cogging torque reduction methods using canceling effect for dual-stator permanent magnet synchronous machines (PMSM). The cogging torque in dual-stator PMSMs can be calculated by the superposition of both cogging torque components from outer and inner stator parts neglecting the saturation effect. If out and inner parts have the same least common multiple of pole/slot number combination, both cogging torque components share the same harmonic contents. Therefore, the cogging torque harmonics from both components can be cancelling or additive, which depends on their phase angle difference. In addition to the relative position shift between inner and outer stators, this paper proposes more options using the canceling effect, such as relative position shift between inner/outer magnet poles, inner/outer slot number pairing, size pairing of inner/outer slot openings, and size pairing of inner/outer pole-arc to pole-pitch ratios. The investigation shows all these methods can effectively reduce the cogging torque. The analysis is verified by direct finite element results.
Modular Stator Flux and Torque Control of Multiphase Induction Motor Drives

Thanks to the advancements in power electronics, the multiphase solutions are gaining a growing attention from the industry. Due to the higher number of phases, the multiphase machines offer more degrees of freedom with respect to the three-phase counterparts. As example, it is possible to configure the drive topology in a modular way in terms of both electrical machine winding and power converter structure. This special feature has led to the development of the “multiple three-phase solutions” in which the multiphase drive is configured as multiple three-phase units operating in parallel. Nevertheless, the literature does not report control solutions able to exploit properly the drive modularity of multiple three-phase drives. Therefore, an innovative control scheme, implementing a modular torque regulation of multiple three-phase induction motor drives, is proposed in this paper. To show the feasibility of the developed control solution, experimental results are provided for a multi-modular power converter feeding a quadruple three-phase induction machine prototype.
Big Data Analytics and the Electric Utility Industry

Big Data Analytics (BDA) has recently seen a significant increase in its application as a powerful tool in supplementing and transforming existing business practices across a variety of industries. The electric utility industry has the potential to utilize BDA due to the large amounts of data the electric utility control centers receive. The data typically streams into the control centers in the form of measurements-analog and status. Some measurements come in every 2–6 sec via SCADA or through newer technology such as phasor measurement units (PMUs), which can produce data at a rate of 30–120 samples per second. The challenge for system operators is to absorb, process and then make precise operational decisions based on these large datasets. There is ample opportunity to utilize BDA to improve situational awareness and ensure system operators are making reliable data driven decisions. This paper provides very useful information to this subject and its application to the electric utility industry.
Research on Acoustic Feature Extraction of Crying for Early Screening of Children with Autism

In the field of early diagnosis of autism children, the current method is mainly based on doctors' clinical observation and experience with the assistance of some quantitive indexes. In this paper, we propose to use acoustic features of crying sound for the early diagnosis of autistic children. Four acoustic features extraction methods (wavelet decomposition coefficient, DWT-MFCC, MFCC and LPCC) and two machine leaning-based classifiers (SVM, CNN) are applied to the crying sounds of autism children aged 2 to 3 years old. Comparison experiments show that MFCC features with SVM and CNN achieved the highest recognition rate, while DWT-MFCC exhibited the most stable performance in the case of five different SNRs. And it also can be seen from the experiments that the convergence speed of MFCC and DWT-MFCC features with CNN is approximately the same. Our research may ultimately help doctors diagnose autism in young children from a speech signal processing perspective.
Metal Artifact Reduction for X-Ray Computed Tomography Using U-Net in Image Domain

Metal artifacts seriously degrade the quality of the CT data and bring great difficulties to subsequent image processing and analysis, which nowadays become a great concern in X-ray CT applications. In this paper, we introduce a U-net-based metal artifact reduction method into CT image domain. The proposed network reduces metal artifacts by learning an end-to-end mapping of images from metal-corrupted CT images to their corresponding artifact-free ground truth images. We design and optimize the network through the simulation experiments. The experimental results show that the proposed method can well reduce metal artifacts of CT images, and this method has higher computational efficiency and greatly shortens the processing time. It avoids complex image preprocessing and can accept input images of any size. Therefore, it can be a more automated way to handle large amounts of data, making it ideal for existing CT workflows.
Design and Analysis of a Bearingless Motor with Passive Axial Suspension Through Null-Flux Coils

This paper describes a bearingless motor with passive axial stabilization. Null-flux figure-8 coils are employed to generate passive restorative forces in the axial direction. Using Finite Element Analysis, passive suspension performance is compared between two proposed models, one with iron core structure and another one with a coreless stator. Analytical results show that the coreless model is more suited for the first prototype machine. This is part of a project that aims to achieve full passive suspension in a bearingless motor.
Bregman-Tanimoto Based Method for Contrast Preserving Decolorization

This paper presents a novel Bregman-Tanimoto based model for faithfully contrast-preserving decolorization. With regard to the defects of the traditional Euclidean metric approaches, the proposed method addresses the problems by introducing a Bregman-Tanimoto metric based maximum function, thus more details, features and visual distinctiveness can be preserved. Moreover, the model extends the solution space of the first-order linear parameter model used for color-to-gray mapping. An extended discrete searching algorithm is developed to solve the proposed model. Experimental results show that the proposed approach outperforms other state-of-the-art methods both quantitatively and qualitatively.
Influence Maximization Under Partially Observable Environments

The problem of influence maximization is a classic subject to study in the field of network science. It is about finding the top-k important individuals in a network for message dissemination under a particular diffusion model. Each year a number of new research papers are published concerning the same issue. However, most of these methods can only operate in situations where the whole graph is visible to the algorithm which is an unrealistic assumption in many cases. There are many cases where the induced network model of a natural phenomenon is associated with missing links. Discarding these links will lead to serious drawbacks in the result. In this work, we have extended the current state of the art influence maximization algorithms by adding a link prediction heuristic step prior to the actual run of the algorithm. For the purpose of link prediction, we have used exponential random graph models also known as ERGM due to their probabilistic link prediction capabilities. We have shown that this heuristic can significantly improve the effectiveness of influence maximization algorithms and in a diffusion scenario we can have a larger number of infected nodes using the seed nodes of the influence maximization algorithm.
MMSE: A Multi-Model Stacking Ensemble Learning Algorithm for Purchase Prediction

Analyzing users’ consumption habits and preferences with big data technology, merchants can collect valuable data about users’ consumption habits which is significant for them to make decisions, with the purpose of improving profitability and optimizing user experience. In this paper, we propose a Multi-Model Stacking Ensemble (MMSE) algorithm to deal with the problem of personalized commodities recommendation. The main components of our algorithm are the data analysis and the model construction. As for the data analysis, we apply data processing technologies to construct the data features. Specifically, we put forward a novel feature model which involves 6 clusters of features, and design a sampling algorithm to balance the ratio of positive and negative samples by the k-means clustering and undersampling. As for the model construction, we establish a two-layer model. In the first layer, we train four different ensemble algorithms as the basic classifiers, and in the second layer, we take XGBoost algorithm as the combiner classifier. Finally, we evaluate the proposed model on the users-commodities behavior data of Alibaba’s M-Commerce platform. It performs better than single ensemble learning algorithms, achieving the performance of F1-score 10.97%.
Modulation Recognition of Underwater Acoustic Communication Signals Based on Data Transfer

In practical applications, there are few labeled training samples in modulation recognition of underwater acoustic communication signals. In order to solve the problem, a data transfer learning method is proposed. The underwater noise in the actual water area is superimposed on the simulation signals to form the training set. The power spectrums of signals are performed as the input of the sparse autoencoder (SAE) network for training. The experimental results show that, when the channel is in good condition, for 2FSK, 4FSK, 8FSK, BPSK, OFDM and LFM, the proposed method can improve the recognition rate to a certain extent compared with the training method without data transfer.
Privacy in Mobile Health Applications for Breast Cancer Patients

Privacy is a major concern for breast cancer patients. When patients use mobile health applications (mHealth apps), many sensitive data are handled by the application developers. General Data Protection Regulation (GDPR) arises as a solution to privacy issues. In this paper, we analyze the privacy policy of a sample of mHealth apps for breast cancer patients, developing a scale to check if GDPR is complied. Despite privacy is a key factor in the adoption of the use of mHealth apps, the low level of compliance with the GDPR of the analyzed applications was quite surprising. Thus, application developers must be concerned about this matter.
An End-to-End Adaptive Input Selection With Dynamic Weights for Forecasting Multivariate Time Series

A multivariate time series forecasting is critical in many applications, such as signal processing, finance, air quality forecasting, and pattern recognition. In particular, determining the most relevant variables and proper lag length from multivariate time series is challenging. This paper proposes an end-to-end recurrent neural network framework equipped with an adaptive input selection mechanism to improve the prediction performance for multivariate time series forecasting. The proposed model, named AIS-RNN, consists of two main components: the first neural network learns to generate context-dependent importance weights to dynamically select the input. The selected input is then fed into the second module for predicting the target variable. The experimental results show that our proposed end-to-end approach outperforms machine learning-based baselines on several public benchmark datasets. The AIS-LSTM model achieves higher performance on a public M3 dataset than the M3-specialized models. Furthermore, the AIS-RNN gives a beneficial advantage to interpret variable importance.
End to End Fingerprint Verification Based on Convolutional Neural Network

Fingerprint recognition has become one of the most reliable ways for human identification due to its uniqueness and consistency. The fingerprint matching problem is formulated as a classification system in which a model is learned to classify every two fingerprints as a genuine or impostor pair. Traditional approaches perform a feature extraction step before matching a fingerprint pair. On the other hand, recently convolutional neural networks (CNNs) have presented exceptional success for many image processing tasks such as face recognition. However, there have been only a few attempts to develop fully CNN methods to deal with challenges in fingerprint recognition problem. In this paper, a CNN-based fingerprint matching method has been developed. A key contribution of the proposed method is to directly learn fingerprint patterns from raw pixels of images. In order to achieve robustness and characterize the similarities comprehensively, incomplete and partial fingerprint pairs were taken into account to extract complementary features. Also, we proposed an end to end CNN approach that contains the feature extraction part of the trained AlexNet network. The network reached an EER of 17.5% on the FVC2002 dataset, that shows better results in comparison to the MinutiaSC and A-KAZE methods.
IPKB: A New Metric for Evaluating Cache Replacement Policies

Nowadays replacement policies draw the attention of researchers. These policies have a direct impact on the cache miss rate and consequently on the performance and power consumption. The hardware overhead grows as the complexity of cache replacement policies increase which results in energy penalties. In this article, we introduce IPKB (Improvement Per Kilo Byte) as a touchstone for replacement policies and then use this new metric to evaluate recent common and credible replacement policies. IPKB indicates both miss rate and hardware overhead, the latter is a better representative of energy consumption. We show that using our metric, the policies with higher miss rate improvement were not the best policy because of their massive hardware overheads.
Automated Image-Based Procedures for Adaptive Radiotherapy

Introduction: We analyze and model changes in tumor radiodensity as well as tumor size for ten head and neck cancer patients during their course of treatment based on PET/CT planning scans and CT replanning scans. Methods: We compare (up to) second order polynomial regression models for repeated measurements with patient speci c as well as population speci c parameters. Results: For the patients included in this study, changes in both tumor size and radiodensity were well described with linear regression models with patient speci c bias and slope. Conclusions: Signi cant changes in both volume and 58 Modeling tumor size and density changes during chemoradiation therapy for head and neck cancer radiodensity were observed. In addition to tumor volume changes, tumor radiodensity changes might need to be taken into account if/when re-planning patients in adaptive photon radiotherapy and could be of substantial importance in adaptive proton therapy as dose distributions are highly dependent on changes in tissue density.
Fluid Lubricated Dexterous Finger Mechanism for Human-Like Impact Absorbing Capability

This letter presents a novel three degree-of-freedom finger mechanism that features excellent impact absorbing performance, high force, and precision comparable to human hands. Inspired by human fingers, it has a simple structure with a minimum number of rigid components: seven rigid frames, several polymer wires, and flexible sheets. For impact absorption, rolling contact joints with a center ligament, instead of conventional revolute joints with bearings or bushings, were used. While this mechanism can be dislocated in the event of off-axis bending or twisting force and impact, it can return afterward without degrading precision. To achieve precise manipulation performance, a unique tendon guiding structure, which enables accurate kinematic modeling even though it is composed of soft and flexible materials, is proposed. To prevent loss of precision and robustness against friction, the whole mechanism is covered by an artificial skin and filled with lubrication liquid, which is similar to human synovial fluid. The developed finger mechanism can exert 40 N fingertip force in a straight pose and has 0.2 mm repeatability of fingertip position under multiple impacts. Various experiments including button clicking and wheel rotation of a computer mouse demonstrated the performance and potential usefulness of the proposed mechanism.
High Efficiency Light Field Compression via Virtual Reference and Hierarchical MV-HEVC

Efficient storage and delivery of the light field (LF) information rely on high performance compression. In this paper, we propose a high efficiency light field compression algorithm that utilizes a hierarchical coding structure with synthetic virtual references. Specifically, a LF image are interpreted as a multi-view sequence that is efficiently compressed using the multi-view extension of high efficiency video coding (MV-HEVC). Using deep neural networks, we synthesize virtual references from reconstructed neighbor frames, they serve as extra reference candidates in our novel hierarchical coding structure. Compared with previous work, the proposed algorithm further exploits the intrinsic similarities in LF images. Experimental results show that the proposed algorithms demonstrate a superior performance that achieves up to 55.2% BD-rate reduction and 2.55dB BD-PSNR improvement compared with the HEVC benchmark and outperforms the state-of-the-art.