Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation and Modeling of Millimeter-Wave Channel at 60 GHz in Indoor Environment for 5G Wireless Communication System

This paper presents the propagation characteristics at 60 GHz for fifth generation (5G) millimeter-wave (mmWave) wireless communication systems. The propagation characteristics of mmWave signal in the indoor radio channels are discussed based on the method of shooting and bouncing ray tracing/image (SBR/IM). Omnidirectional path loss models, received power and root-mean-square (RMS) delay spreads statistics are analyzed in terms of line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The results show that the path loss exponent (PLE) values in LOS environments vary between 1.56 and 1.78 and the PLE value is 3.87 in NLOS scenario. The RMS delay spreads values vary between 2.1 ns and 6.6 ns. Furthermore, the normal distribution and the Gaussian distribution models best fit the received power and the RMS delay spreads at 60 GHz, respectively.
Evaluating the Influence of User Searches on Neighbors

Big Data rise made traditional data management techniques inadequate in many real life scenarios. In particular, the availability of huge amounts of data pertaining to user suggestions and searches calls for advanced analysis strategies in order to profitably leverage these data. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch. In this paper, we describe our proposal for analysing the way user searches and suggestions influence their social environment in order to quickly identify users able to spread their influence across the network. It is worth noting that, gathering information about user preferences is crucial in several scenarios like viral marketing, tourism promotion and food education.
Fetching Relevant Images and Videos in Keyword Based Search Mechanism with Hypergraph Learning and Similarity Matching

This paper is an attempt for the work done for image and video retrieval system. Content-Based Image Retrieval(CBIR) and Content-Based Video Retrieval(CBVR), has attracted researchers from various research fields like, computer vision, artificial intelligence, human factors, machine learning, image processing, man-machine modeling to name a few. Existing methods have revealed certain flaws like noisy data, often leading to display of irrelevant images or videos. In our system we have used Hypergraph Learning for images and Similarity Matching for videos. All the above challenges are addressed by retrieving relevant images and videos in response to user's keyword based search approach. User can search by attributes present during search or user can search by new attribute which gets added in the attribute list in the database and we can have ranking of the retrieved results and get relevant data. Experimentation is carried out from Flickr database for images and videos under consideration are those which are available on YouTube. A database of images and videos that cover user's interest in diverse domains is designed and used in experimentation.
Can We Use Survival Data from Cancer Registries to Learn about Disease Recurrence? The Case of Breast Cancer

Background: Population-representative risks of metastatic recurrence are not generally available because cancer registries do not collect data on recurrence. This article presents a novel method that estimates the risk of recurrence using cancer registry disease-specific survival. Methods: The method is based on an illness–death process coupled with a mixture cure model for net cancer survival. The risk of recurrence is inferred from the estimated survival among the noncured fraction and published data on survival after recurrence. We apply the method to disease-specific survival curves from female breast cancer cases without a prior cancer diagnosis and with complete stage and hormone receptor (HR) status in Surveillance, Epidemiology and End Results registries (1992–2013). Results: The risk of recurrence is higher for women diagnosed with breast cancer at older age, earlier period, more advanced stage, and HR-negative tumors. For women diagnosed at ages 60–74 in 2000–2013, the projected percent recurring within 5 years is 2.5%, 9.6%, and 34.5% for stages I, II, and III HR-positive, and 6.5%, 20.2%, and 48.5% for stages I, II, and III HR-negative tumors. Although HR-positive cases have lower risk of recurrence soon after diagnosis, their risk persists longer than for HR-negative cases. Results show a high degree of robustness to model assumptions. Conclusions: The results show that it is possible to extract information about the risk of recurrence using disease-specific survival, and the methods can in principle be extended to other cancer sites. Impact: This study provides the first population-based summaries of the risk of breast cancer recurrence in U.S. women. Cancer Epidemiol Biomarkers Prev; 27(11); 1332–41. ©2018 AACR.
Investigation of DC-DC Boost Converter for Reliability of Operational Planning

Presentation of power electronic-based converters into the various applications and mainly in vertical and high power applications is an important issue. Another critical challenge in this field is reliability issues of the overall systems. This paper considers a reliability problem for an interleaved boost converter with respect to minimizing cost of the converter. As the power semiconductor switches are highly vulnerable, their reliability analysis is of high importance in reliability studies. The failure of one-power switches can reduce the system reliability. A possible approach to enhance the reliability of DC-DC converters is employing an interleaved technique. This technique nevertheless increases the implementation cost. Reducing the volume and cost with maximum reliability is a necessary issue that less attention has been paid to literature. In the problem analysis, the levelized costs of components and reliability formulations are considered. The proposed approach for design the studied converter considered reliability and cost indexes as the main goal of design. Numerical results show that cost and reliability two indices of contradiction.
Hybrid Technique for Denoising Multi Environment Noise in Speech Processing

In general noise control is critical issue in signal processing. Almost every signal that we are receiving at the receiver side of any communication system is somehow affected by noise. Noise is the unwanted part of the signal. In any communication system filtering is needed for rejecting all other unwanted frequencies present in the received signal and gives the desired signal. To denoising the different types noise signals requires different noise removing methods. This paper introducing a hybrid technique for noise reduction in speech signals those are corrupted by noise in multi environments i.e. street, airport, car and train noises are processed by Gaussian window and kalman filter. In order to access the accuracy of this combination of filters, the performance of this hybrid technique gives evaluation of both Mean Square Error and Peak Signal to Noise Ratio at the input to the corresponding values at the output of the system. The PSNR value of the proposed system for noise level of 10dB is affected by street noise is achieved 35.457339 as output. The results obtained by using this hybrid technique are better than the other techniques.
Fault Current Analysis of Type-3 WTs Considering Sequential Switching of Internal Control and Protection Circuits in Multi Time Scales During LVRT

As Type-3 wind turbine (WT) has already become a typical type of highly penetrated power sources in modern power systems, characterizing its properties during grid fault is a basic requirement for system analysis. However, many significant factors have not been considered in the existing analytical methods and results, such as various switching of internal control and protection circuits in low voltage ride through (LVRT) solutions. In this paper, performance of Type-3 WT during LVRT is first illustrated and summarized as the sequential switching characteristic in multi time scales, viz., instant control, ac control, dc voltage control, and rotor speed control time scales. Thus, for magnitude drops in stator voltage, a mathematical model is proposed to understand this characteristic and fault current analysis's features. Then, an analytical method, based on the operational inductance, is proposed so that fault current components, with the sequential switching considered, can be depicted with a unified and simple analytical expression. By identifying magnitude of symmetrical current, the transient and steady-state equivalent circuits of Type-3 WT are proposed to estimate momentary current and electrical power approximately. Finally, analytical results are verified by comparisons with real-time simulation results of a detailed 1.5 MW WT model in RT-LAB.
Multi Segmentation Method for Hemorraghic Detection

The beneficial of technology of Computer Tomography (CT) Scan has been recognized useful to examine all human organs, particularly for human’s brain. CT Scan can detect stroke on hemorrhagic type stroke because it shows very clear different between white’s matter and gray’s matter. The segmented area is handy in providing human’s brain information that shows which is the bleeding area and which is not bleeding. The intensity of pixels characterized by the maximum number of the segmented regions can be used to differentiate bleeding from healthy tissue. The focus of the study was to calculate the area of hemorrhage, the volume of hemorrhage, the value of Hounsfield Unit (HU). Differences in HU value in each patient signify the difference between bleeding due to blockage (infarction) or due to rupture of blood vessels of the brain. Image data information is obtained by conducting ground truth on a CT Scan image indicated by the hemorrhagic stroke with the dominant white matter feature in the brain. Ground truth performed presents an evaluation of several segmentation methods such as contour segmentation, adaptive otsu, watershed segmentation and clustering using Fuzzy C Mean method using image of CT Scan using 20 patient data indicated by the hemorrhagic stroke. Segmentation is done on 2D CT Scan data in axial position. The evaluation method was performed to obtain the accuracy value of bleeding image reconstruction by comparing with ground truth done by the radiologist.
An Efficient Approach for Fault Detection and Fault Tolerant Control of Wind Turbines

This paper presents an efficient technique for monitoring and control of wind turbines. Fault tolerant control (FTC) is designed using Parity-Space approach to diagnose the sensor faults in the wind turbine. Based on the fault detection system an FTC scheme is proposed to accommodate and reconfigurable the control signals. The main contribution of this study is to develop a monitoring and control system of wind turbine, which reduces the system components as well as to increase the reliability and safety of wind turbines. The sensors which considered in this study are pitch position, generator speed and rotor speed sensors. Real data of wind measurement and real parameters of wind turbine is used to illustrate the proposed scheme.
Comparative Analysis of Bone Marrow-derived Mast Cell Differentiation in C57BL/6 and BALB/c Mice

ABSTRACT Allergic diseases have increased in the last three decades. Mast cells play a critical role in allergic diseases along with allergen-specific immunoglobulin E (IgE). Following mast cell degranulation elicited by ligation of the IgE-FcεRI receptor complex with allergen, allergic reactions are followed by various symptoms such as vascular hyperpermeability, mucous secretion, itching, sneezing, wheezing, rashes, fever, and anaphylactic shock. Susceptibility or inclination to allergy varies depending on individual genetic traits and living environment, and it has long been believed that such an inclination is determined by an immunologic balance of T helper cell types. Mouse strains also have different susceptibilities to allergy. Similar to T helper cells and macrophages, it is not known whether mast cells can also be divided into two different types between mouse strains. In this study, we prepared bone marrow-derived mast cells from BALB/c and C57BL/6 mice and examined their cellular properties. Cellular response to IL-3 and the process of mast cell differentiation from bone marrow cells were different on the basis of cell surface marker molecules. BALB/c-derived cells more efficiently exhibited degranulation than did C57BL/6-derived cells following both calcium ionophore and receptor crosslinking. These functional differences persisted even after a longer cell culture for 8 weeks, suggesting a difference in cell-autonomous characteristics. These results support the concept that mast cells also have different cell types dependent on their genetic background. Abbreviations: Ab: antibody; BMMC: bone marrow-derived mast cell; DNP: dinitrophenyl; FACS: fluorescence-activated cell sorter; FCS: fetal calf serum; FITC: fluorescein isothiocyanate; FSC: forward scatter; HRP: horseradish peroxidase; HSA: human serum albumin; Ig: immunoglobulin; IL: interleukin; MIP-2: macrophage inflammatory protein-2; MCP: mast cell protease; PE: phycoerythrin; PerCP: Peridinin chlorophyll protein complex; SNP: single nucleotide polymorphisms; SSC: side scatter; Th: T helper; TNF-α: tumor necrosis factor alpha
What is pre‐fibrotic myelofibrosis and how should it be managed in 2018?

The 2016 World Health Organization (WHO) classification for myeloproliferative neoplasms (MPN) divided myelofibrosis (MF) into pre‐fibrotic (pre‐MF) and overt‐MF categories. This new classification, particularly the entity pre‐MF, has been a subject of discussion between experts. Important questions have been raised in recent years, such as the need for bone marrow trephine for diagnosis; how this is interpreted and the weighting given to it in assigning a diagnosis; determination of prognosis for pre‐MF patients; including which scoring system to use and, ultimately, an evidence‐based management plan for this group of patients. Many pre‐MF patients present as young adults, with thrombocytosis, elevated lactate dehydrogenase levels and increased bone marrow fibrosis (i.e. ≥ grade 1). Current management strategies differ in view of age, comorbidities and bone marrow features and the opinion of the managing clinicians. Prognostic scoring systems have some limitations regarding this entity, and at the present time there is limited information about the overall survival and incidence of progression to overt‐MF and acute leukaemia for pre‐MF. In this clinically focussed article, we review the main characteristics of this new disease category in view of the current published literature and illustrate our discussion with some real patient cases. Lastly, we propose a management strategy for patients to whom this diagnostic label is applied.
A Study on Design Method of the Switching System for Force Control

This paper proposes the design method of a switching force controller by using a data-driven controller design method which derives a controller only using an input/output data set of a plant. The force control system consists of the contact environment as well as the robot itself. In addition, the contact environment might vary according to change of the contact point. The force controller must be designed so as to adapt in consideration of various environments. Therefore, this paper focuses on the switching force controller to solve the above problem, and adopts the data-driven controller design method and some machine learning techniques to design the switching controller. The effectiveness of the proposed design method is verified by simulation examples.
Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing

Convolutional neural networks (CNNs) such as encoder-decoder CNNs have increasingly been employed for semantic image segmentation at the pixel-level requiring pixel-level training labels, which are rarely available in real-world scenarios. In practice, weakly annotated training data at the image patch level are often used for pixel-level segmentation tasks, requiring further processing to obtain accurate results, mainly because the translation invariance of the CNN-based inference can turn into an impeding property leading to segmentation results of coarser spatial granularity compared with the original image. However, the inherent uncertainty in the segmented image and its relationships to translation invariance, CNN architecture, and classification scheme has never been analysed from an explicitly spatial perspective. Therefore, the authors propose measures to spatially visualise and assess class decision confidence based on spatially dense CNN predictions, resulting in continuous decision confidence surfaces. They find that such a visual-analytical method contributes to a better understanding of the spatial variability of class score confidence derived from weakly supervised CNN-based classifiers. They exemplify this approach by incorporating decision confidence surfaces into a processing chain for the extraction of human settlement features from historical map documents based on weakly annotated training data using different CNN architectures and classification schemes.
Differences in the Electromyographic Activity of Lower-Body Muscles in Hip Thrust Variations.

Collazo García, CL, Rueda, J, Suárez Luginick, B, Navarro, E. Differences in the electromyographic activity of lower-body muscles in hip thrust variations. J Strength Cond Res XX(X): 000-000, 2018-Coaches often use variations of an exercise to train a specific muscle. The purpose of this study was to analyze motor patterns in 4 variations of one of the most popular strength training exercises for the lower body: the barbell hip thrust. Seven experienced personal trainers performed a series of 8 repetitions of each variation with a load of 40% one repetition maximum. Subjects rested 3' between series. Electromyographic (EMG) muscle activity was measured in the rectus femoris, vastus medialis; vastus lateralis; gluteus maximus; gluteus medius; biceps femoris; and semitendinosus. Variations of the hip thrust exercise were performed by changing the position of the feet (feet were moving away from the body) and the direction of force exerted by subjects (intentional force aimed at hip's external rotation and knee's flexion). Repeated-measures analysis of variance revealed significant differences in EMG in all muscles except for the gluteus medius, where no differences were observed among variations. The results obtained suggest that hip thrust variations have different motor patterns, which can be exploited to adapt an exercise to the individual needs of each athlete.
Optimal Multi-Objective Allocation of Fast Charging Stations

In the paper, a new multi-temporal methodology is proposed for the determination of the daily demand profiles of the Fast Charging Stations and the calculation of the charging process waiting time. The methodology has been implemented within a Multi-Objective optimization algorithm, based on the Non-dominated Sorting Genetic Algorithm, for the simultaneous allocation and sizing of Fast Charging Stations using a probabilistic load flow. Results on the planning of two coupled distribution and transportation systems are presented to demonstrate the feasibility and consistency of the developed model, which gives the opportunity to make a proper trade-off between the contrasting interests of different stakeholders, i.e. the Distribution Network Operator, the Fast Charging Stations owner and the Plug-in Electric Vehicles drivers.
Tensor-on-Tensor Regression

ABSTRACT I propose a framework for the linear prediction of a multiway array (i.e., a tensor) from another multiway array of arbitrary dimension, using the contracted tensor product. This framework generalizes several existing approaches, including methods to predict a scalar outcome from a tensor, a matrix from a matrix, or a tensor from a scalar. I describe an approach that exploits the multiway structure of both the predictors and the outcomes by restricting the coefficients to have reduced PARAFAC/CANDECOMP rank. I propose a general and efficient algorithm for penalized least-squares estimation, which allows for a ridge (L2) penalty on the coefficients. The objective is shown to give the mode of a Bayesian posterior, which motivates a Gibbs sampling algorithm for inference. I illustrate the approach with an application to facial image data. An R package is available at https://github.com/lockEF/MultiwayRegression.
The Deep Neural Network Algorithm Based on Meteorological Features is in the Medium and Long Term

In order to improve the prediction accuracy of medium and long term wind speed, the deep neural network algorithm is applied to the medium and long term wind speed forecasting in this article. Automatically extract the pattern of wind speed change through powerful nonlinear mapping ability of deep neural networks. In the specific process, take historical wind speed and meteorological as input, deep convolution neural network framework is used to train the model. Through the deep structure of the network, we can learn the internal relationship between the sequences and achieve the prediction of the future wind speed series. The effect of the results is perfect after verification. The prediction result of wind speed in some wind field in Ganhekou of Gansu Province shows, the wind speed prediction deviation of the two models of CNN+MLP and CNN+LSTM are at a relatively low level in most cases. Compared with the traditional weather forecast, improved accuracy of prediction, better acceptance of the abnormal data, and stronger generalization ability of the model. In summary, this method has certain practical value.
NCC Feature Matching Optimized Algorithm Based on Constraint Fusion

In this paper, a binocular stereo vision three-dimensional (3D) reconstruction algorithm is proposed. In order to reduce the computation in feature extraction process, it begins with selecting candidate corner points, and then uses this as the center to establish the search area. Finally, scale invariant feature transform (SIFT) algorithm is used to extract corner points. In the process of stereo matching, the rough matching point pairs obtained from the Normal Cross Correlation (NCC) algorithm are applied to feature constraints to get the precise matching point pairs so that the final experiment realizes the 3D reconstruction of objects.
Nonlinear PID-Type Controller for Quadrotor Trajectory Tracking

A novel proportional-integral-derivative (PID)-type motion controller for a quadrotor is introduced in this paper. A rigorous analysis of the closed-loop system trajectories is provided, and gain tuning guidelines are discussed. Real-time experimental results consisting of the implementation of a PID-based scheme, a sliding-mode controller, and the new scheme are given. Gains are selected so that the three tested controllers present the same energy consumption. In order to assess the robustness of the controllers tested, experiments are carried out in the presence of disturbances in one of the actuators. Specifically, the disturbance consists in attenuating the force delivered. Better tracking accuracy is obtained with the introduced nonlinear PID-type algorithm.
Design, Analysis and Experiment of A Non-humanoid Arm Exoskeleton for Lifting Load

Aiming to the interference with human body problem of the traditional arm exoskeleton, a non-humanoid arm exoskeleton is presented. The non-humanoid arm exoskeleton has a lager motion space, which is 20.8% more than the humanoid arm exoskeleton. The statics of the non-humanoid structure is analyzed. The minimal interaction force control strategy between the arm exoskeleton and human is used in the control system for lifting load experiment, and the subjects can lift the load of 5Kg easily. Additionally, the response time of adjusted control system reduce 86.25% according the control system without PID.