Miracle-dz/UniFAR-FTUs huggingface dataset:
High Efficient Deep Feature Extraction and Classification of Spectral-Spatial Hyperspectral Image Using Cross Domain Convolutional Neural Networks

Recently, numerous remote sensing applications highly depend on the hyperspectral image (HSI). HSI classification, as a fundamental issue, has attracted increasing attention and become a hot topic in the remote sensing community. We implemented a regularized convolutional neural network (CNN), which adopted dropout and regularization strategies to address the overfitting problem of limited training samples. Although many kinds of the literature have confirmed that it is an effective way for HSI classification to integrate spectrum with spatial context, the scaling issue is not fully exploited. In this paper, we propose a high efficient deep feature extraction and the classification method for the spectral-spatial HSI, which can make full use of multiscale spatial feature obtained by guided filter. The proposed approach is the first attempt to lean a CNN for spectral and multiscale spatial features. Compared to its counterparts, experimental results show that the proposed method can achieve 3% improvement in accuracy, according to various datasets such as Indian Pines, Pavia University, and Salinas.
Localization of Autonomous Cars Using Multi-Sensor Data Fusion

This paper introduces a localization method where GPS, inertial measurement unit (IMU), odometer and 3D lidar are the main sensors. GPS is mainly used as a global position, and is also used as an initial position for map matching. The map matching is realized through the Normal Distributions transform (NDT)point cloud matching algorithm, by which the optimal transformation of the lidar point cloud and the established map is obtained. In case of GPS signal loss, the dead reckoning which is realized by the fusion of IMU and odometer is also applied. By comparing the experimental results to the accurate traj ectory, it is found that the positioning accuracy has been improved using the algorithm herein.
Robust Hierarchical Learning for Non-Negative Matrix Factorization With Outliers

Desirable properties of extensions of non-negative matrix factorization (NMF) include robustness in the presence of noises and outliers, ease of implementation, the guarantee of convergence, operation in an automatic fashion that trades off the balance between data approximation and model simplicity well, and the capability to model the inherently sequential structure of time-series signals. The state-of-the-art methods typically have only a subset of these aforementioned properties and seldom simultaneously possess them all. In this paper, we propose a novel approach that provides all these desirable properties by extending the automatic relevance determination framework in NMF from Tan and Févotte. Starting from an objective function derived from the maximum a posterior estimation of a Bayesian model, we develop majorization–minimization algorithms that work effectively to determine the correct model order, regardless of the impact of noise and outliers. Subsequently, we give a rigorous convergence analysis of the proposed algorithms. Moreover, convolutive bases are also incorporated in the basic model so that it is able to capture the richness of temporal continuity. We perform experiments on both synthetic and real-world data sets to show the efficiency and robustness of our approach.
Simulation of water turbine integrated with electrical generator

The paper presents the analysis of the small hydropower plant working at variable speed. The hydro-set that consists of the guide vanes and propeller turbine integrated with the permanent magnet synchronous generator is simulated by using Computational Fluid Dynamics (CFD) in Ansys Fluent v18.0. The k-ε and k-ω SST models, as well as the one-equation Spalart-Allmaras (SA) model, were tested. The comparison showed the significant divergence of the calculation results. The turbulence model selection influences the average value of the power and also the speed for which the power is maximal.
Software vs Hardware Implementations for Real-Time Operating Systems

In the development of the embedded systems a very important role is played by the real-time operating system (RTOS). They provide basic services for multitasking on small microcontrollers and the support to implement the deadlines imposed by critical systems. The RTOS used can have important consequences in the performance of the embedded system. In order to eliminate the overhead generated by RTOS, the RTOS primitives have begun to be implemented in hardware. Such a solution is the nMPRA architecture (Multi Pipeline Register Architecture - n degree of multiplication) that implements in hardware of all primitives of an RTOS. This article makes a comparison between software RTOS and nMPRA systems in terms of response time to an external event. For comparison, we use three of the most commonly used RTOS in developing embedded systems: FreeRTOS, uC/OS-III and Keil RTX. These RTOSs are executed on a microcontroller that works at the same frequency as the implementations of the nMPRA architecture on a FPGA system.
Effect of Temperature on the Efficacy of Fungicide and Biocontrol Agent with Incidence of Stem Gall Disease of Coriander

Field experiments were conducted in 2011 during Rabi season at Main. Experiment Station of Department of Vegetable Science (Main Campus), Narendra Deva University of Agriculture and Technology, Narendra Nagar (Kumarganj), Faizabad in the North Indo-Gangetic plains of Eastern Uttar Pradesh to evaluate disease control efficacy of five chemical fungicide Carbendazim, Ridomil, Blitox-50, Hexaconazole, Propiconazole and two biocontrol agent Trichoderma, Pseudomonas by seed treatment with foliar spray when they were applied at different growth stages of coriander in relation to temperature. Experimental results showed that all treatments except in Ridomil T4, Hexaconazole T6 and Propiconazole T7, the disease appeared on 5th February when minimum temperature was 7 and maximum temperature was 26.0°C. The disease progressed slowly till 6th March at minimum temperature of 11 and maximum temperature of 30°C. In all the treatments the disease progressed after 16th March with a minimum temperature of 13 and maximum temperature of 32.5°C with an increase in maximum temperature from 32.5 to 36.5°C disease intensity increased. This increase in the intensity of stem gall disease was checked at a temperature above 36.5°C. In Ridomil T4, Hexaconazole T6 and Propiconazole T7 the treatment has delayed the incidence of disease till 25th February and showed similar pattern of growth as inother treatment.
Using Alice Software with the Expository Method: A Pilot Study

— This article presents a learning environment design that compares the effectiveness of using Alice software with conventional programming languages in the scope of initial programming learning. The model proposes a quasi-experimental design in three stages: i) a pre-test to assess the development of logical reasoning in experimental (n=11) and control (n=11) groups; ii) learning programming with the Alice software and pseudocode or exclusively with pseudocode, in the experimental and control group respectively, both using a expository teaching methodology; iii) applying a validated programming test to assess student knowledge, mediated by logical reasoning in both groups. The pilot study indicated no significant differences in the programming test between experimental and control groups (p > 0.05). These results indicate that the effectiveness of the Alice software in programming knowledge might not work with the expository method.
Learning a Layered Graph with a Maximal Number of Paths Connecting Source and Sink

We discuss the problem of ranking objects sampled from in general r ordered categories. A ranking model for this kind of data can be visualized as a (layered) ranking graph having a single source and sink, and the number of paths connecting source and sink is seen as a ranking-based performance measure. In this article we consider learning such a graph as a structured output in a convex optimization framework. To this end, a new kernelbased algorithm for layered ranking is presented by optimizing the number of distinct paths connecting the source and sink. Although the resulting quadratic program has a huge number of constraints, we show that it can be efficiently optimized with a cutting plane algorithm and graph-based methods. The algorithm scales cubic in the number of training objects.
A Qualitative Study Exploring the Needs of Turkish Mothers in the Pediatric Intensive Care Unit

Ad dress for Cor res pon den ce Dilek Beytut PhD, Girne American University, School of Nursing, Girne, Cyprus Phone: +90 533 843 83 59 E-mail: dilekbeytut@gmail.com ORCID ID: orcid.org/0000-0002-2921-9810 Re cei ved: 27.02. 2018 Ac cep ted: 21.05. 2018 1Girne American University, School of Nursing, Girne, Cyprus 2Dokuz Eylül University Faculty of Nursing, Department of Pediatric Nursing, İzmir, Turkey 3Muğla Sıtkı Koçman University Fethiye Faculty of Health Sciences, Department of Nursing, Muğla, Turkey Dilek Beytut1, Gülçin Özalp Gerçeker2, Gonca Karayağız Muslu3
Role of seminal fluid in sexual transmission of HIV-1

This study analysed the reservoir of HIV-1 in semen and the effects of seminal plasma on the functional and phenotypic characteristics of dendritic cells (DCs), both in relation to immunoregulatory capabilities and susceptibility to infection with HIV. The reservoir of HIV-1 in semen was defined in order to assess the feasibility of ‘sperm washing’ as a means of reducing the risk of transmission in HTV-1-discordant couples. The fact that neither viral RNA or DNA could be detected in spermatozoa and the lack of expression of CD4 both at the protein and mRNA levels suggests that spermatozoa are not susceptible to HTV-1 infection. Viral RNA was detected in seminal plasma and both RNA and DNA were detected in non-sperm cells. As a result of this study, sperm washing is a service that is now available at the Chelsea & Westminster Hospital to HTV-l-discordant couples. This service has resulted in several births and no seroconversions in any woman who has undergone the procedure. The effects of seminal plasma on the phenotype and function of monocyte-derived DCs were investigated. Seminal plasma had suppressive effects on the allostimulatory capacity of DCs and such effects appeared to be due to down- regulation of co-stimulatory molecule expression on these cells. The suppressive effect of seminal plasma was abrogated by the removal of lipids. However, prostaglandins on their own did not have suppressive effects on DC function, suggesting that other seminal components are required to induce theobserved induction of suppression. The suppressive effect of seminal plasma on the allostimulatory function of DCs was overcome with TNF-a, as did the presence of HIV. The expression of HIV co-receptors was assessed on DCs. TNF-a induced maturation of DCs as demonstrated by reduced expression of CCR5 and increased CXCR4. Seminal plasma also enhanced expression of CXCR4 and infection with X4 strains of HIV, but down-regulated CCR5 expression and infection with R5 strains of HIV. Therefore, the regulation of co-receptor expression by DCs was associated with their susceptibility to infection with a HIV strain displaying the corresponding co-receptor usage. In summary, semen is an important vehicle for transmission of HIV. Seminal plasma was demonstrated to have profound effects on DC phenotype, maturation and function. Such alterations would influence the outcome, in terms of HIV transmission, of the interaction between the DC and HIV-1 at the mucosal surface.
Interactive Big Data Analytics Platform for Healthcare and Clinical Services

A Big Data Platform (BDA) with Hadoop/MapReduce technologies distributed over HBase (key-value NoSQL database storage) and generate hospitalization metadata was established for testing functionality and performance. Performance tests retrieved results from simulated patient records with Apache tools in Hadoop’s ecosystem. At optimized iteration, Hadoop distributed file system (HDFS) ingestion with HBase exhibited sustained database integrity over hundreds of iterations; however, to complete its bulk loading via MapReduce to HBase required a month. The framework over generated HBase data files took a week and a month for one billion (10TB) and three billion (30TB), respectively. Apache Spark and Apache Drill showed high performance. However, inconsistencies of MapReduce limited the capacity to generate data. Hospital system based on a patient encounter-centric database was very difficult to establish because data profiles have complex relationships. Recommendations for key-value storage should be considered for healthcare when analyzing large volumes of data over simplified clinical event models.
Design and modeling of a piezo-actuated positioning mechanism

We are involved in the design and control of a 1-dimensional positioning mechanism for a range of 50 /spl mu/m and an accuracy of 30 pm. Only piezoelectric actuators can be used to manage such small displacements. However, these actuators show hysteretic behavior and lengthening saturation. The extremely small displacements are measured by means of capacitive sensors. In order to design a controller a model of the positioning mechanism and the actuators is developed. Contrary to the existing literature, in our model the hysteresis is described by a differential equation. The mechanical characteristics of the design, and therefore also of the model, are chosen such that they are practically realizable and advantageous for controller design. The final nonlinear fifth order state space model is believed to be suitable for a nonlinear control technique like feedback linearization.
Implementasi Sistem Monitoring Detak Jantung Dan Suhu Tubuh Manusia Secara Wireless

Detak jantung dan suhu tubuh merupakan tanda vital yang secara rutin diperiksa oleh rumah sakit untuk mengetahui tanda klinis yang berguna untuk memperkuat diagnosis suatu penyakit. Pada prosesnya, pemeriksaan detak jantung dan suhu tubuh di beberapa rumah sakit masih menggunakan sistem manual dimana seorang perawat harus datang ke kamar pasien untuk melihat dan mencatat detak jantung dan suhu tubuh pasien. Sistem ini kurang efektif karena memakan banyak waktu. Pada penelitian ini dibuat sebuah sistem monitoring detak jantung dan suhu tubuh manusia secara wireless. Sistem ini menggunakan pulse sensor untuk mendeteksi detak jantung, LM35 untuk mendeteksi suhu tubuh, untuk pemroses datanya menggunakan Arduino nano dan memanfaatkan NRF24L01 sebagai media pengiriman data secara wireless. Sistem ini mendeteksi detak jantung dan suhu tubuh secara realtime. Data hasil olahan kemudian ditampilkan pada sebuah aplikasi. Informasi yang diberikan pada aplikasi meliputi detak jantung per menit, suhu tubuh, dan indikator kondisi detak jantung dan suhu tubuh pasien. Selain itu aplikasi dilengkapi fitur menyimpan detak jantung dan suhu tubuh pada waktu tertentu. Dari hasil pengujian, tingkat keberhasilan sistem dalam mendeteksi detak jantung adalah 97.17%. Sedangkan dalam mendeteksi suhu tubuh tingkat keberhasilan sistem adalah 99.28%. Untuk pengiriman data, sistem dapat melakukan pengiriman data dengan lancar pada jarak maksimal 15 meter dengan penghalang. Untuk penyimpanan pada database, sistem dapat menyimpan data sesuai dengan yang diharapkan.
Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

This study demonstrates the use of mobile phone data to derive country-wide mobility patterns. We identified significant locations of users such as home, work, and other based on a combined measure of frequency, duration, time, and day of mobile phone interactions. Consecutive mobile phone records of users are used to identify stay and pass-by locations. A stay location is where users spend a significant amount of their time measured through their mobile phone usage. Trips are constructed for each user between two consecutive stay locations in a day and then categorized by purpose and time of the day. Three measures of entropy are used to further understand the regularity of user’s spatiotemporal mobility patterns. The results show that user’s in a high entropy cluster has high percentage of non-home based trips (77%), and user’s in a low entropy cluster has high percentage of commuting trips (49%), indicating high regularity. A set of doubly constrained trip distribution models is estimated. To measure travel cost, the concept of a centroid point that assumes the origins and destinations of all trips are concentrated at an arbitrary location such as the centroid of a zone is replaced by multiple origins and destinations represented by cell tower locations. Note that a cell tower location can only be used as trips origin/destination location when a stay is detected. The travel cost measured between cell tower locations has resulted in shorter trip distances and the model estimation shows less sensitivity to the distance-decay effect.
Age of Substance Use Initiation Among Treatment Admissions Aged 18 to 30

X Nearly two fifths (38.6 percent) of admissions that initiated substance use at the age of 11 or younger reported a cooccurring mental disorder. Initiating substance use during childhood or adolescence is linked to substantial long-term health risks. Early (aged 12 to 14) to late (aged 15 to 17) adolescence is generally regarded as a critical risk period for the initiation of alcohol use,1,2,3 with multiple studies showing associations between age at first alcohol use and the occurrence of alcohol abuse or dependence.2,3,4 Moreover, there is evidence across a range of other substances—including marijuana, cocaine, other psychostimulants, and inhalants—that the risk of developing dependence or abuse is greater for individuals who initiate use of these substances in adolescence or early adolescence than for those who initiate use during adulthood.3,4,5 For example, 2012 National Survey on Drug Use and Health data indicate that among those adults who first tried marijuana at the age of 14 or younger, 13.2 percent were classified with illicit drug dependence or abuse; this percentage was 6 times higher than that for adults who first used marijuana at the age of 18 or older.6 In fact, among adolescents, the transition from initiation to regular use of alcohol, marijuana, and other drugs often occurs within 3 years.7 Examining patterns in the age of drug or alcohol initiation among persons in treatment for substance abuse may increase understanding of the characteristics of those who initiate substance use during their childhood or adolescence and highlight potential ways to optimize prevention and treatment efforts.
Sensorless step positioning of hydraulic linear actuator

Abstract The paper presents a design, working principle and simulation of a sensorless position system whose hydraulic linear stepper actuator is controlled by a combination of single binary on/off valves. For dynamic modelling and digital simulation of stepper actuator, the bond graph method was used. Preliminary simulation tests were conducted to determine the dynamic characteristics and dynamic properties of the stepper actuator.
Friction Model of Industrial Robot Joint with Temperature Correction by Example of KUKA KR10

The quality of industrial robots essentially depends on the properties of their kinematic couples. This research has involved conducting an experimental study of the friction torque in a joint of the KUKA KR10 industrial robot and building its model. It has been established that the largest impact on friction in the joint is caused by its axial load and velocity, as well as the temperature of the mechanism, which is generally not homogeneous. It is not possible to measure temperature fields in the joints of a serial industrial robot directly. This study has set forth a method to estimate friction torque taking into account the temperature factor indirectly. For this, we have used the motor temperature available for measuring in combination with special periodical motions, performed by the robot, during which we estimated the actual friction torque in the joint and calculated a temperature correction based on our findings.
Manipulator Grasping Based on Object Detection

To make sure that manipulator can perform well on novel environment, a new grasping approach based on object detection is proposed. A pose estimation network is used as usual to predict the grasping pose of the object while an object detection network is added before it as the input information of the pose estimation. This combination of object detection and pose estimation improves the grasping accuracy by 28% and shows grasping robustness to objects which are not seen by manipulator before.
A Selective Generation of Hybrid Random Numbers via Android Smart Phones

The True Random Number Generator (TRNG) is an important module for countless cryptographically secure appliances. TRNG generation technique requires custom made hardware with quantum phenomena, which on the whole are expensive research components. A secure and sound TRNG design is anticipated at this time, which is based on the permutation of sensor feeds and combination of noise patterns from the peripherals of any Android smartphone. The source for this generator is the selected features from the in-built hardware components namely accelerometer, gyroscope, magnetometer, WiFi signal measure, cell tower information, GPS coordinates and battery thermal noise. Additionally, Process ID (PID) of the corresponding Android processes is given as key vector to augment the entropy and also addresses some of the security concern like confidentiality, integrity and availability of the authentic sources. Hardware-Based Random Number Generator (HRNG) can be achieved by combing the source of the user, their corresponding parameters and the measure of imperfection. Keywords— PID; TID; Mobile Sensors; TRNG; Thermal Noise; HRNG;
Detection of General Obstacles by Subtraction of Road-Surface with Past In-Vehicle Camera Images

Recently, research and commercialization of driving support technology is an active topic. In light of the above background, we focus on a technique to detect forward obstacles from in-vehicle camera images. Most existing techniques detect obstacles that could be expected beforehand, such as a pedestrian or a car. However, there could also exist various unexpected obstacles on the road. Therefore, it is necessary to detect general obstacles. Hence, this paper proposes a method for detecting general forward obstacles by subtraction of road-surface regions between present and past in-vehicle camera images. The proposed method first absorbs the gap of the road-surface regions due to the differences of the running speed and the running position in the lane, and determines the correspondence of the road-surface regions between the present and the past in-vehicle camera images. Then, the proposed method detects obstacles by subtracting the corresponding road-surface regions. Experiments were conducted using actual in-vehicle camera images. The results show that the proposed method could detect obstacles on the road accurately by subtracting the present and past in-vehicle camera images. Moreover, it was confirmed that the distances to the detected obstacles were large enough to avoid the obstacles while a vehicle runs at a standard velocity in an urban area.