Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Observer on SO(3) for Attitude Estimation on Rotating Earth Using Single Vector Measurements

This letter presents a novel attitude estimation solution, built on $SO(3)$ , that resorts to single measurements of a constant inertial vector, in addition to angular velocity readings provided by a set of three high-grade fiber optic rate gyros, which are assumed to be sensitive to the angular motion of the Earth. This approach contrasts with typical attitude solutions that require either a single but time-varying inertial vector, or measurements of two non-collinear inertial vectors. The designed nonlinear observer features only one tuning scalar parameter that, in view of the region of convergence of the rotation matrix error, is shown to render the proposed solution almost globally asymptotically stable. Extensive simulation results with realistic noise, including Monte Carlo, are presented that allow to assess the achievable performance.
Convolution Neural Network based Transfer Learning for Classification of Flowers

Flower plays an extremely important role in our life, which has high research value and application value. The traditional methods of flower classification is mainly based on shape, color or texture features, and this methods needs people to select features for flower classification lead to the accuracy of classification is not very high. This paper aims to develop an effective flower classification approach using convolution neural network and transfer learning. In this paper, based on VGG-16, VGG-19, Inception-v3 and ResNet50 models were used to compare the network initialization model with the transfer learning model. The results show that transfer learning can effectively avoid deep convolution networks are prone to local optimal problems and over-fitting problems. Compared with the traditional methods, the accuracy of flower recognition on Oxford flowers dataset is obviously improved, and has better robustness and generalization ability.
Applying the CG-logistic Regression Method to Predict the Customer Churn Problem

The aim of this paper is to study a new prediction method for the churn problem for airlines. For this end, a conjugate gradient logistic regression (CG-logistic regression) model is built, which integrates a conjugate gradient method with a logistic regression model. First, we classify churn and non-churn customers utilize the logistic regression model to, and then the conjugate gradient iterative method is applied as the optimum solution using the logistic regression model. At last, we present the outcomes of a simulative assessment and prove that the presented method is conducive to analyzing the churn problem for airlines.
Caching Policies for Transient Data

This work focuses on designing caching policies for transient data, i.e., data which can be used to serve requests only for a finite duration of time after which it becomes redundant. We first characterize the fundamental limit on the performance of caching policies for transient data and characterize the performance of traditional caching policies like LRU for this setting. Traditional caching policies often make decisions based on the popularity of the data being cached. We propose a new caching policy which uses both the popularity and the residual lifetime (time remaining before the data becomes redundant) to make caching decisions. We show that in the setting where data being cached is transient, our policy outperforms traditional caching policies.
Evaluation of the Impact of Deforestation on the Radio Wave Propagation Near the Large Antenna System in the Calculation of Sanitary Protection Zones

The influence of ground irregularities on the radio wave propagation for many decades is an actual topic of research and is stimulated by important practical problems related to the operation of various types of communication, radar, radio navigation, remote sensing of the earth's surface. Hills, ravines, forest vegetation, buildings can act as roughnesses of the earth's surface. The use of the parabolic equation method for modeling the propagation of radio waves over an irregular surface of a complex profile with forest massifs is presented. The possibility of using the obtained numerical results for the assessment of sanitary protection zones and deforestation near large antenna systems is discussed.
Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering—A Topical Review

Over the past decades, significant improvements have been made in the field of computational human phantoms (CHPs) and their applications in biomedical engineering. Their sophistication has dramatically increased. The very first CHPs were composed of simple geometric volumes, e.g., cylinders and spheres, while current CHPs have a high resolution, cover a substantial range of the patient population, have high anatomical accuracy, are poseable, morphable, and are augmented with various details to perform functionalized computations. Advances in imaging techniques and semiautomated segmentation tools allow fast and personalized development of CHPs. These advances open the door to quickly develop personalized CHPs, inherently including the disease of the patient. Because many of these CHPs are increasingly providing data for regulatory submissions of various medical devices, the validity, anatomical accuracy, and availability to cover the entire patient population is of utmost importance. This paper is organized into two main sections: the first section reviews the different modeling techniques used to create CHPs, whereas the second section discusses various applications of CHPs in biomedical engineering. Each topic gives an overview, a brief history, recent developments, and an outlook into the future.
A Review of Mobile Crowdsourcing Architectures and Challenges: Toward Crowd-Empowered Internet-of-Things

Crowdsourcing using mobile devices, known as mobile crowdsourcing, is a powerful approach incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. The problems that can be tackled include the use of geographically distributed tasks, and mobile sensing using the collective wisdom of the crowd. However, the implementation of mobile crowdsourcing applications has been found to be challenging to users due to the nature of dynamic sensing, crowd engagement with data distribution, and a process of data verification. In this paper, we provide an extensive survey of the literature on mobile crowdsourcing research, highlighting the aspects of particular concerns in terms of implementation needs during the development, architectures, and key considerations for their development. We present a taxonomy based on the key issues in mobile crowdsourcing and discuss the different approaches applied to these issues. We also provide a critical analysis of some challenges and suggest directions for future work. In particular, with the future Internet-of-Things in view, we generalize the notion of mobile crowdsourcing to thing crowdsourcing, where crowdsourcing can be issued from smart Internet-connected things that need to harness the human resources to solve problems.
Accurate Oracle Classification Based on Deep Convolutional Neural Network

The primary issue in the study of oracle is how to recognize and understand oracle characters. Therefore, the recognition of oracle is an significant research in the oracle field. In this paper, we firstly exploy different convolutional neural network (CNN) architectures, which are aim at implementing automatic feature extraction and classification to improve the performance of oracle recognition. We also show that the Gabor feature extraction method, which is demonstrated effectively to improve the precision of classification. To reduce overfitting caused by insufficient training samples, we generate more training samples from original training data sets by using a variety of data argument techniques. Compared to the raw image data, traditionally directional feature is able to better performance in network. Experimental results show that AlexNet models achieve recognition accuracy of 97.82%.
Evolutionary 4G/5G Network Architecture Assisted Efficient Handover Signaling

Future wireless networks are expected to be ultra-dense and heterogeneous not just in terms of the number and type of base stations but also in terms of the number of users and the application types they access. Such a network architecture will require mobility management mechanisms that adapt rapidly to these highly dynamic network characteristics. In particular, the optimality of the handover signaling within these future network architectures will be extremely critical given their density and heterogeneity. In this paper, the optimality is relevant for both the total amount of signaling created and the total delay per handover process. In this paper, we first present a novel and optimized message mapping and signaling mechanism for the handover preparation and failure phases. We also develop a novel handover failure aware preparation signaling methodology, which accounts for the possibility of a handover failure and grants additional enhancements to the handover preparation and failure signaling phases. Through the analytical framework provided in this paper, we conduct studies to quantify the performance gains promised by the proposed mechanisms. These studies cover myriad handover scenarios as identified by 3GPP and use the statistics from cellular network operators and vendors. We then develop the idea and analytical framework for network wide analysis, in which the network wide processing cost and network occupation time for various handover failure rates are computed. Finally, we propose an evolutionary network architecture that facilitates the proposed signaling mechanism as well as assists operators in maintaining a manageable capital expenditure. It combines the current day and 3GPP proposed 5G network architecture with the software-defined networking approach. As a result, we argue that the proposed mechanisms are viable and outperform the legacy handover signaling mechanisms in terms of latency incurred, total network occupation time, number of messages generated, and total bytes transferred.
An Improved Segmentation for Raw G-Band Chromosome Images

Karyotyping is one of the most common procedure in cytogenetics to identify and evaluate the presence of genetic defects or disorders. Developing an automatic analysis algorithm for this procedure was studied by numerous other researchers before. One of the fundamental parts of this process is the segmentation of chromosomes, which is followed by classification. Despite recent improvements in deep neural networks and image classification, high-quality segmentation is essential to achieve accurate classification results. Still, automatic segmentation and extrication of touching and overlapping chromosomes are current problems. In the light of the above, this paper is intended to present an automatic segmentation and separation of G-band human chromosomes. we have tried to present an algorithm to overcome all the difficulties such as accurate thresholding, separation of touching and overlapping chromosomes. Unlike previous studies, we are also focusing on issues that may increase the segmentation quality beyond the separation of overlapping chromosomes. In this work, we present an end-to-end segmentation of chromosome images. This process includes noise removal and rejection of unwanted objects, separating fore and background, a binary watershed approach to divide intuitively and easily detectable clusters, finding a geodesic path between chromosomes in remaining clusters and finally disentangling of overlapped and complex clusters. We also built a user interface and let a little human intervention to detect unnoticed clusters. We have tested the proposed method on 145 chromosome images that contain 6678 chromosomes and 6532 (97.8%) of them have been correctly extracted.
Effect of Unsteady Surface Wave on the Performance of Tidal In-Stream Power Systems

Tidal in-stream power generation is based on current speed which is more predictable compared to wind speed, but the performance of tidal in-stream devices may be affected by unsteady surface waves. This work examines the influence of unsteady surface waves on the performance of tidal in-stream energy systems using real data for tidal currents from Minas Passage site, Bay of Fundy, Nova Scotia, Canada. Two concepts are used to test the correlation between waves and tidal currents with two different tools for both types of energy variables. The first concept is the speed of tidal currents. The relationship between the tidal current speed and the maximum wave height is confirmed. The second concept considers the wave direction. For the most cost-effective system, considering the effects of surface waves is needed. Results confirm the distortion effect of unsteady surface wave on the tidal in-stream speed.
Tanner  $(J,L)$  Quasi-Cyclic LDPC Codes: Girth Analysis and Derived Codes

Girth plays an important role in the design of low-density parity-check (LDPC) codes. Motivated by the works on the girth of some classes of Tanner quasi-cyclic (QC) LDPC codes, e.g., Tanner (3, 5), (3, 7), (3, 11), and (5, 7) codes, we, in this paper, study the girth of Tanner <inline-formula> <tex-math notation="LaTeX">$(J,L)$ </tex-math></inline-formula> QC-LDPC codes where <inline-formula> <tex-math notation="LaTeX">$J$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> can be any two positive integers. According to the sufficient and necessary conditions for the existence of cycles of lengths 4, 6, 8, and 10, we propose an algorithm to determine the girth of Tanner <inline-formula> <tex-math notation="LaTeX">$(J,L)$ </tex-math></inline-formula> QC-LDPC codes with finite code lengths. Through the analysis of the obtained girth values, we generalize the laws of the girth distributions of Tanner <inline-formula> <tex-math notation="LaTeX">$(J,L)$ </tex-math></inline-formula> QC-LDPC codes. Furthermore, based on the exponent matrices of Tanner <inline-formula> <tex-math notation="LaTeX">$(J,L)$ </tex-math></inline-formula> QC-LDPC codes with known girths, we employ the column selection method and/or the masking technique to construct binary/nonbinary LDPC codes. The numerical results show that the constructed LDPC codes have good performance under iterative decoding over the additive white Gaussian noise channel.
Effect of Anisotropic Metasurface Linings on Circular Waveguide

The rapid development of metasurfaces has become a new momentum driving novel electromagnetic applications. When they were loaded inside circular waveguides as linings, many intriguing phenomena were observed. From the point view of the surface impedance of a lining, the real part of the impedance, or called surface resistance, induces attenuation effect to the waves propagating through the waveguide, and the imaginary part, or surface reactance, causes shifts to the dispersion of the waves. Some literatures have discussed the effects of the surface impedance, but most of them involved isotropic conditions only. In this paper, dispersion and attenuation characteristics of the waveguides affected by anisotropic metasurface linings are analyzed through mathematical derivations and numerical validations, and new approaches are proposed for predicting the performances. This work may pave a way for new applications in circular waveguides, such as single-mode bandwidth expansion, degenerate mode avoidance, mode filters, and so on.
Improvement of Power Factor in a Double-Side Linear Flux-Modulation Permanent-Magnet Motor for Long Stroke Applications

Cost-effective and high-performance linear motors attract more and more attention for many applications. This paper proposes a novel double-side linear flux-modulation permanent-magnet (DS-LFMPM) motor with low cost, high thrust force density, and high power factor. The novelty of the proposed DS-LFMPM motor is to improve power factor by using double-side topology. Its topology is presented, and its operation principle is investigated. From the harmonic spectrums of the flux density in the air gap before and after the modulation of the mover teeth and the stator teeth, it can be concluded that the proposed motor has good flux-modulation effect. Also, the expression of the orders of the harmonic spectrums is given. The design process of sizes and structure is particularly explained. The electromagnetic performances are predicted by using the finite-element method and the proposed motor is prototyped for verification. The results show that the proposed DS-LFMPM motor can produce high thrust force density and high power factor.
Small-Signal Stability of Islanded-Microgrids With DC side Dynamics of Inverters

This paper addresses the small-signal stability analysis of inverter-based microgrids when operating in islanded mode. The dc dynamics of droop-controlled inverters are taken into consideration revealing new small-signal stability requirements that were not investigated before. First, a linearized state-space model of a previously developed non-linear system is formulated. The traces of the eigenvalues of the linearized system are compared to traces of previous models to demonstrate how the dc dynamics of the inverters affects the stability of the system. Time-domain simulations of a microgrid are performed in MATLAB to test the validity of the results of the linearized model. Results point to a direct relation between the minimum values of the dc voltage source and the microgrid load to maintain system stability.
Future Generation Computer Systems

Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. A Computed Tomography (CT) scan used to find the position of tumor and identify the level of cancer in the body. The current study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA). The deep features extracted from aCTlungimagesandthendimensionalityoffeatureisreducedusingLDRtoclassifylungnodulesaseither malignant or benign. The ODNN is applied to CT images and then, optimized using Modified Gravitational Search Algorithm (MGSA) for identify the lung cancer classification The comparative results show that the proposed classifier gives the sensitivity of 96.2%, specificity of 94.2% and accuracy of 94.56%. © 2018ElsevierB.V. Allrightsreserved.
Eliminating reflection from type theory

Type theories with equality reflection, such as extensional type theory (ETT), are convenient theories in which to formalise mathematics, as they make it possible to consider provably equal terms as convertible. Although type-checking is undecidable in this context, variants of ETT have been implemented, for example in NuPRL and more recently in Andromeda. The actual objects that can be checked are not proof-terms, but derivations of proof-terms. This suggests that any derivation of ETT can be translated into a typecheckable proof term of intensional type theory (ITT). However, this result, investigated categorically by Hofmann in 1995, and 10 years later more syntactically by Oury, has never given rise to an effective translation. In this paper, we provide the first effective syntactical translation from ETT to ITT with uniqueness of identity proofs and functional extensionality. This translation has been defined and proven correct in Coq and yields an executable plugin that translates a derivation in ETT into an actual Coq typing judgment. Additionally, we show how this result is extended in the context of homotopy type theory to a two-level type theory.
EBP: An Efficient Broadcast Protocol for Warning Message Dissemination in VANETs

Vehicular Ad-hoc Networks (VANETs) enable vehicle-to-vehicle communications to share relevant road information with various applications. Road safety is one of the main objectives of VANETs that has attracted great interest from researchers. Other so-called comfort applications have also been studied which can improve driving experience and passenger safety. In all these applications, sharing of warning messages can help drivers minimize accidents and congestions, and plan better itinerary during the congestion situations or the anticipation of potential and highly hazardous events. In this paper, we present an efficient broadcast protocol (EBP) for broadcasting warning messages in VANETs. As an improved strategy for alert data dissemination, EBP can be applied in whatever the nature of the risk, with a particular focus on mobile dangers. We first conduct an in-depth analysis and evaluation, under different conditions, of the existing approaches and mechanisms used for information dissemination in VANETs. Then, we point out their drawbacks and design the EBP to avoid these drawbacks. As a result, the EBP is an improved, well-justified and more effective protocol. We validate it by simulation experiments under various scenarios.
Improving <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Subspaces via Coherence Pursuit

Subspace clustering is a powerful generalization of clustering for high-dimensional data analysis, where low-rank cluster structure is leveraged for accurate inference. <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Subspaces (KSS), an alternating algorithm that mirrors <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-means, is a classical approach for clustering with this model. Like <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-means, KSS is highly sensitive to initialization, yet KSS has two major handicaps beyond this issue. First, unlike <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-means, the KSS objective is NP-hard to approximate within any finite factor for a large enough subspace rank. Second, it is known that the <inline-formula><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula> subspace estimation step is faulty when an estimated cluster has points from multiple subspaces. In this paper, we demonstrate both of these additional drawbacks, provide a proof for the former, and offer a solution to the latter through the use of a robust subspace recovery (RSR) method known as coherence pursuit (CoP). While many RSR methods have been developed in recent years, few can handle the case where the outliers are themselves low rank. We prove that CoP can handle low-rank outliers. This and its low computational complexity make it ideal to incorporate into the subspace estimation step of KSS. We demonstrate on synthetic data that CoP successfully rejects low-rank outliers and show that combining CoP with <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Subspaces yields state-of-the-art clustering performance on canonical benchmark datasets.