Miracle-dz/UniFAR-FTUs huggingface dataset:
SINR Analysis of Different Multicarrier Waveforms over Doubly Dispersive Channels

Wireless channels generally exhibit dispersion in both time and frequency domain, known as doubly selective or doubly dispersive channels. To combat the delay spread effect, multicarrier modulation (MCM) such as orthogonal frequency division multiplexing (OFDM) and its universal filtered variant (UF-OFDM) is employed, leading to the simple per-subcarrier one tap equalization. The time-varying nature of the channel, in particular, the intra-multicarrier-symbol channel variation induces spectral broadening and thus inter-carrier interference (ICI). Existing works address both effects separately, focus on the one effect while ignoring the respective other. This paper considers both effect simultaneously for cyclic prefix (CP)-, zero padded (ZP)- and UF-based OFDM with simple one tap equalization, assuming a general wireless channel model. For this general channel model, we show that the independent (wide sense stationary uncorrelated scatter, WSSUS) selectivity in time and frequency starts to intertwine in contrast to the ideal cases with single selectivity. We derive signal-to-interference-plus-noise ratio (SINR) in closed form for arbitrary system settings and channel parameters, e.g., bandwidth, delay- and Doppler-spread. With the SINR analysis, we compare the three MCM schemes under different channel scenarios.
Sensor Fusion for Magneto-Inductive Navigation

Magneto-inductive navigation is an inexpensive and easily deployable solution to many of today’s navigation problems. By utilizing very low frequency magnetic fields, magneto-inductive technology circumvents the problems with attenuation and multipath that often plague competing modalities. Using triaxial transmitter and receiver coils, it is possible to compute position and orientation estimates in three dimensions. However, in many situations, additional information is available that constrains the set of possible solutions. For example, the receiver may be known to be coplanar with the transmitter, or orientation information may be available from inertial sensors. We employ a maximum a posteriori estimator to fuse magneto-inductive signals with such complementary information. Further, we derive the Cramér-Rao bound for the position estimates and investigate the problem of detecting distortions caused by ferrous material. The performance of the estimator is compared to the Cramér-Rao bound and a state-of-the-art estimator using both simulations and real-world data. By fusing magneto-inductive signals with accelerometer measurements, the median position error is reduced almost by a factor of two.
A Signal Separation Method for Exceeding the Channel Capacity of BPSK Input

The field of the coded modulations witnessed its golden era as the simulated achievable bit rates (ABRs) were getting close to the channel capacities of the finite alphabet inputs. However, when working at a single channel, the previous researches are mostly restricted in scope of single input and single output. In contrast, for achieving higher spectral efficiency, the present authors propose a method that builds upon the parallel transmission of two signals that are separated at the receiver. The signal separation is enabled by, so called, the double mapping modulation working through Hamming- to Euclidean space. Consequently, the ABR summation of the two signals is found higher the channel capacity of BPSK input. Both the theoretical- and simulation result confirm this approach.
PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification

A new machine learning methodology, called successive subspace learning (SSL), is introduced in this work. SSL contains four key ingredients: 1) successive near-to-far neighborhood expansion; 2) unsupervised dimension reduction via subspace approximation; 3) supervised dimension reduction via label-assisted regression (LAG); and 4) feature concatenation and decision making. An image-based object classification method, called PixelHop, is proposed to illustrate the SSL design. It is shown by experimental results that the PixelHop method outperforms the classic CNN model of similar model complexity in three benchmarking datasets (MNIST, Fashion MNIST and CIFAR-10). Although SSL and deep learning (DL) have some high-level concept in common, they are fundamentally different in model formulation, the training process and training complexity. Extensive discussion on the comparison of SSL and DL is made to provide further insights into the potential of SSL.
Effects of Age, Fitness, and Exercise on the Control of Cerebral Blood Flow

VII ACKNOWLEDGEMENTS . IX LIST OF TABLES . XI LIST OF FIGURES . XIII LIST OF ABBREVIATIONS . XIX DISCLOSURE OF PUBLICATIONS . XXI CHAPTER ONE .
Efficient Architecture for Digital Image Processing Based on EPLD Muzhir

Digital image processing becomes a wide area of applications that introduce in each part of real life. One of the big challenge of digital image processing is how to speed up the processing speed in order to support the real time application. The main objective of this work is to design a simple method that achieve the digital image processing with in the real time video rate. The architecture of real time digital image processing approach needs high speed components to process a big amount of pixels at minimum time as possible. The implemented approach used Erasable Programmable Logic Device (EPLD) to realize the real time digital image processing. This approach combine both parallel processing and pipeline processing in the architecture. This approach leads to good architecture performance.
Uniform Convergence of Double Sequences of Functions

In this work, we discuss various kinds of I2-uniform convergence for double sequences of functions and introduce the concepts of I2 and I2-uniform convergence, I2-uniformly Cauchy sequences for double sequences of functions. Then, we show the relation between them. 1. Background and Introduction The concept of convergence of a sequence of real numbers has been extended to statistical convergence independently by Fast [11] and Schoenberg [28]. This concept was extended to the double sequences by Mursaleen and Edely [21]. A lot of development have been made in this area after the works of Šalát [27] and Fridy [13, 14]. Furthermore, Gökhan et al. [16] introduced the notion of pointwise and uniform statistical convergent of double sequences of real-valued functions. In general, statistically convergent sequences satisfy many of the properties of ordinary convergent sequences in metric spaces [11, 13, 14, 25]. Çakan and Altay [4] presented multidimensional analogues of the results presented by Fridy and Orhan [12]. Throughout the paperN denotes the set of all positive integers and R the set of all real numbers. The idea of I-convergence was introduced by Kostyrko et al. [18] as a generalization of statistical convergence which is based on the structure of the ideal I of subset of the set of natural numbers. Nuray and Ruckle [23] indepedently introduced the same with another name generalized statistical convergence. Das et al. [5] introduced the concept of I-convergence of double sequences in a metric space and studied some properties of this convergence. Balcerzak et al. [3] discussed various kinds of statistical convergence and I-convergence for sequences of functions with values in R or in a metric space. Gezer and Karakuş [15] investigated I-pointwise and I-uniform convergence and I∗-pointwise and I∗-uniform convergence of function sequences and examined the relation between them. Dündar and Altay [8] investigated the relation between I2-convergence and I2-convergence of double sequences of functions defined between linear metric spaces. Some results on I-convergence may be found in [2, 6, 19, 20, 22, 29]. In this work, we discuss various kinds of uniformly ideal convergence for double sequences of functions with values in R or in a metric space. We introduce the concepts of I2, I2-uniform convergence, I2uniformly Cauchy sequences for double sequences of functions and show the relation between them. 2010 Mathematics Subject Classification. Primary 40A30; Secondary 40A35
Mispricing Factors

A four-factor model with two “mispricing” factors, in addition to market and size factors, accommodates a large set of anomalies better than notable four- and five-factor alternative models. Moreover, our size factor reveals a small-firm premium nearly twice usual estimates. The mispricing factors aggregate information across 11 prominent anomalies by averaging rankings within two clusters exhibiting the greatest return comovement. Investor sentiment predicts the mispricing factors, especially their short legs, consistent with a mispricing interpretation and the asymmetry in ease of buying versus shorting. A three-factor model with a single mispricing factor also performs well, especially in Bayesian model comparisons.
Tilburg University Memory-based machine translation and language modeling

We describe a freely available open source memory-based machine translation system, . Its translation model is a fast approximate memory-based classifier, trained to map trigrams of sourcelanguage words onto trigrams of target-language words. In a second decoding step, the predicted trigrams are rearranged according to their overlap, and candidate output sequences are ranked according to a memory-based language model. We report on the scaling abilities of the memory-based approach, observing fast training and testing times, and linear scaling behavior in speed and memory costs. e system is released as an open source soware package1, for which we provide a first reference guide.
Source discriminative word lexicon for translation disambiguation

This paper presents a source discriminative word lexicon that performs translation disambiguation for individual source words using structural features, such as context and dependency relations in the sentence. The individual translation predictions are combined into sentence scores that are used in N -best list re-ranking to improve the translation output of a state of the art phrase-based machine translation system. The approach is used to improve explicitly the translation of word categories that require grammatical agreement to hold in the target language after translation, e.g. pronouns, as well as subjects and verbs. The results show that the translation predictions provided by the source discriminative word lexicon increase the prediction accuracy by up to 10%. The translation quality can be improved by up to 0.6 BLEU points on English-German translation.
Title Validating Two Novel Equivalent Impedance Estimators

Certain approaches to appraising voltage stability use an equivalent impedance to characterise the wider power system. This letter proposes two new ways of inferring an appropriate equivalent impedance from a power system’s admittance matrix. Continuation power flow simulations are used to validate the quality of the new estimators, and to benchmark them against some extant approaches.
Rank Restricted Semidefinite Matrices

6 We study the closure of the projection of the (nonconvex) cone of rank restricted positive 7 semidefinite matrices onto subsets of the matrix entries. This defines the feasible sets for semidef8 inite completion problems with restrictions on the ranks. Applications include conditions for 9 low-rank completions using the nuclear norm heuristic. 10
Title Evidence against global attention filters selective for absolute bar-orientation in human vision Permalink

The finding that an item of type A pops out from an array of distractors of type B typically is taken to support the inference that human vision contains a neural mechanism that is activated by items of type A but not by items of type B. Such a mechanism might be expected to yield a neural image in which items of type A produce high activation and items of type B low (or zero) activation. Access to such a neural image might further be expected to enable accurate estimation of the centroid of an ensemble of items of type A intermixed with to-be-ignored items of type B. Here, it is shown that as the number of items in stimulus displays is increased, performance in estimating the centroids of horizontal (vertical) items amid vertical (horizontal) distractors degrades muchmore quickly and dramatically than does performance in estimating the centroids of white (black) items among black (white) distractors. Together with previous findings, these results suggest that, although human vision does possess bottom-up neural mechanisms sensitive to abrupt local changes in bar-orientation, and although human vision does possess and utilize top-down global attention filters capable of selecting multiple items of one brightness or of one color from among others, it cannot use a top-down global attention filter capable of selecting multiple bars of a given absolute orientation and filtering bars of the opposite orientation in a centroid task.
The Influence factors of lecturer performance in implementing teaching-learning process in Midwifery Study Program of Magelang - Semarang Health Polytechnic, academic year 2005/2006

Performance is an employee efficacy in completing a job. Lecturer’s performance can be evaluated from the ability in planning, implementing, and evaluating the teaching-learning process. Magelang Midwifery Study Program as one of diploma III educational institutions in Midwifery Major of Semarang Health Polytechnic has not optimized yet in learning process, besides there was a limitation in number of lecturers who have academic qualification.The research aim is to analyze the influence of lecturer’s performance in implementing teaching-learning process in Midwifery Study Program of Magelang for academic year 2005/2006 The research is an observational. The method of the study is cross sectional. Population of the study is a total population of fulltime lecturers in Midwifery Study Program of Magelang. Bivariate analysis was testing by chi-square test and multivariate analysis was tested by logistic regression analysis test. Descriptive analysis revealed young respondent’s age as 53.6%, low educational level (S1, Diploma IV, and Diploma III) was 75%, and there was only 25% of high educational level (S 2). Work experience in low category was 53.5%, high work-motivation was 53.6%, work satisfaction in low level was 53.6, perception of reward within good category was 60.7% and perception of supervision in good category was as much as 53.6%. Bivariate analysis result showed there was a significant correlation between age (p=0.002), education (p=0.038), motivation (p=0.002), satisfaction (p=0.023), perception of compensation (p=0.020), supervision perception (p=0.023) with lecturer’s performance in implementing teaching-learning process. Multivariate analysis result showed that there was an influence of motivation toward lecturer’s performance (p=0.008) with Exp value (b) = 25.670, and satisfaction to lecturer’s performance (p=0.057) with Exp value (b) = 11.209. Sumber Utama : www.mikm. undip.ac. id
Title Robust Transceiver with Tomlinson-Harashima Precoding forAmplify-and-Forward MIMO Relaying Systems

In this paper, robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplifyand-forward (AF) multiple-input multiple-output (MIMO) relaying systems is investigated. At source node, THP is adopted to mitigate the spatial intersymbol interference. However, due to its nonlinear nature, THP is very sensitive to channel estimation errors. In order to reduce the effects of channel estimation errors, a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. With novel applications of elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the transceiver design problem reduces to a much simpler one with only scalar variables which can be efficiently solved. Finally, the performance advantage of the proposed robust design over non-robust design is demonstrated by simulation results.
Efficacy of cognitive-behavioral therapy for patients with temporomandibular disorder pain-systematic review of previous reports

Cognitive-behavioral therapy (CBT) was previously demonstrated to be the predominant psychological treatment for individuals with chronic pain conditions and is also thought to be effective for reducing pain in temporomandibular disorder (TMD) patients. We systematically reviewed randomized control trials (RCTs) performed to examine the effects of CBT on chronic pain in TMD patients. The PubMed search engine of the National Institutes of Health was employed to search for related studies published in English up to March 10, 2016. The initial search identified 138 reports, then following evaluation of the titles and abstracts, 57 publications were further screened according to the inclusion criteria. Next, we reviewed the full text of 28 RCTs, with 6 studies finally selected for this study. We could not perform a meta-analysis of the 6 RCTs because of the different CBT protocols and follow-up periods used in each study. However, meta-analysis of 2 RCTs that compared CBT and standard treatment with standard treatment alone for pain levels was performed. Subjects who received both CBT and standard treatment showed no significant reduction in pain as compared to those who received standard treatment alone after 12 months (standardized mean difference = 0.14, 95% confidence interval = -0.011 to 0.38, P > 0.05). In conclusion, the effectiveness of CBT for pain in TMD patients was not shown in the present meta-analysis of RCTs. Additional prospectively controlled and randomized long-term clinical trials are necessary to establish the efficacy of CBT for chronic pain caused by TMD.
Optical diffraction tomography with fully and partially coherent illumination in high numerical aperture label-free microscopy

Quantitative label-free imaging is an important tool for the study of living micro organisms which during the last decade has attracted wide attention from the optical community. Optical diffraction tomography (ODT) is probably the most relevant technique for quantitative label-free 3D imaging applied in widefield microscopy in the visible range. The ODT is usually performed using spatially coherent light illumination and specially designed holographic microscopes. Nevertheless, the ODT is also compatible with partially coherent illumination and can be realized in conventional wide-field microscopes by applying refocusing techniques as it has been recently demonstrated. Here we compare these two ODT modalities underlining their pros and cons and discussing the optical setups for their implementation. In particular, we pay special attention to a system which is compatible with conventional wide-field microscope and can be used for both ODT modalities. It consists of two easily attachable modules: the first one for sample illumination engineering based on digital light processing technology and another one for focus scanning by using an electrically-driven tunable lens. This hardware allows for a programmable selection of the wavelength and the illumination design, as well as it provides fast data acquisition. Its performance is experimentally demonstrated in the case of ODT with partially coherent illumination providing specklefree 3D quantitative imaging.
Peningkatan Ekspresi Vascular Endothel Growth Factor dan Mitogen Activating Protein Kinase Plasenta Tikus yang Dipapar Carbon Black ( ( INCREASING EXPRESSION ON VASCULAR ENDOTHELIAL GROWTH FACTOR AND MITOGEN ACTIVATING PROTEIN KINASE IN PLACENTA RATS EXPOSED CARBON BLACK )

Carbon black is one of the components of particulate matter (PM), known as cytotoxic, causing inflammation in the respiratory and cardiovascular system, and able to pass through the placental barrier, which may affect the fetus. The effects caused by pollutants depend on the intensity and time length of the exposure. The objective of this study is to identify Vascular endothelial growth factor (VEGF) and mitogen-Activating Protein Kinase (MAPK) as an indicator of maternal placental inflammation in pregnant rats (Rattus norvegicus) exposed to carbon black with immuno-histochemical technique. This study was a laboratory experiment with three treatment groups and seven replications using pregnant female white rats (Rattus norvegicus). Pregnant female rats were exposed to carbon black with a dosage of 523 mg / m3 and 1064 mg / m3 during pregnancy of day 6 up to day 11. The next phase was the observation and examination of the expressions of VEGF and MAPK by immunohistochemical staining toward placenta of the white rats exposed to carbon black. The result of the study showed an increase in the expression of VEGF in the Jurnal Veteriner Maret 2017 Vol. 18 No. 1 : 59-68 pISSN: 1411-8327; eISSN: 2477-5665 DOI: 10.19087/jveteriner.2017. 18.1. 59 Terakreditasi Nasional, Dirjen Penguatan Riset dan Pengembangan, online pada http://ojs.unud. ac.id/php.index/jvet Kemenristek Dikti RI S.K. No. 36a/E/KPT/2016
Vitamin D deficiency and anthropometric indicators of adiposity in school-age children : a prospective study 1 – 3

Background: Cross-sectional studies have indicated that vitamin D serostatus is inversely associated with adiposity. It is unknown whether vitamin D deficiency is a risk factor for the development of adiposity in children. Objective: We investigated the associations between vitamin D serostatus and changes in body mass index (BMI; in kg/m), skinfoldthickness ratio (subscapular-to-triceps), waist circumference, and height in a longitudinal study in children from Bogota, Colombia. Design: We quantified plasma 25-hydroxyvitamin D [25(OH)D] concentrations in baseline samples of a randomly selected group of 479 schoolchildren aged 5–12 y and classified vitamin D status as deficient [25(OH)D concentrations ,50 nmol/L], insufficient [25(OH)D concentrations 50 and ,75 nmol/L], or sufficient [25(OH)D concentrations 75 nmol/L]. We measured anthropometric variables annually for a median of 30 mo. We estimated the average change in each anthropometric indicator according to baseline vitamin D status by using multivariate mixed linear regression
Unsupervised learning on functional data with an application to the analysis of U.S. temperature prediction accuracy

Unsupervised learning techniques are widely applied in exploratory analysis as the motivation of further analysis. In functional data analysis, two typical topics of unsupervised learning are functional principal component analysis and functional data clustering analysis. In this study, besides reviewing the developed unsupervised learning techniques, we extend unsupervised random forest clustering method to functional data and detect its shortages and strength through comparisons with other clustering methods in simulation studies. Finally, both proposed method and developed unsupervised learning techniques are conducted on a real data application: the analysis of the accuracy of the U.S. temperature prediction from 2014 to 2017.
Modifications of DNA clamps and their role in DNA damage management

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