Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Acidity in Accelerated Aged Insulating Paper Impregnated with Different Insulating Oil

The presence of acidity in oil-impregnated paper insulation accelerate its degradation process. Currently, there are no international standards that regulate the assessment of inside insulation paper. At present, the paper condition is assumed to be represented by oil conditions. However, a recent experiment found that the partition coefficients of various acids produce very different concentrations of acid in the paper. This study will discuss the detail of the determination of the acid number of insulation paper. The samples of (Kraft) paper insulation impregnated with two different types of oils, i.e., uninhibited oil, and inhibited oil are used for the measurements. The paper samples treated in the same aging conditions; it was delivered with thermal stress at 130 °C in various aging times until 15 weeks. The investigation is carried out in 9 different methods. To distinguish one method to others, selection of measurement parameters such as the mass of the paper sample, the mass of pure water, paper sample processing, filtering process, the mass of extract and material to be titrated is done in a different way.
An Analog-Memoryless Near Sensor Computing Architecture for Always-On Intelligent Perception Applications

In always-on intelligent visual perception applications, Convolution Neural Network algorithm has been widely applied. However, the implementation of it's processing is limited by hardware resources and energy budgets, which come from data access, interface overhead and inflexible configuration. In this paper, we present a novel analog architecture that implements low-precision Binary Neural Network and fuses with CMOS image sensor for always-on applications. By enabling current-mode computation in analog domain, the overhead of analog-to-digital converter as well as analog buffer is eliminated. And in order to relax the overhead of CIS, conventional correlating-double-sampling is moved to Computation Module. Moreover, the architecture is also dynamic-configurable, which is optimized specifically for CIS data flow. Simulations with HSPICE show that the peak energy efficiency of proposed architecture can achieve 13.91 TOPS/W at the 8-bit percision of multiplication-and-accumulation analog computation, while accuracy loss is only about 5.66%.
Delay-Oriented Task Scheduling and Bandwidth Allocation in Fog Computing Networks

Fog computing can aggregate the computing resources to handle the unprecedented amounts of data and becomes a promising technology in the future 5G smart Internet of Things (IoT) networks. This paper considers an IoT video data analysis system where smart IoT cameras can transmit all data to the base station or analyze the data locally. After receiving smart cameras offloading data, the base station can partially redistribute the analyzing task to the smart user equipment. The smart cameras and base station task offloading scheme and the uplink-downlink bandwidth allocation are jointly optimized to minimize the system level delay. The problem is a mixed integer non-linear problem, and the objective function contains the sum of several segmented maximum, which makes it very challenging to solve. Firstly, the smart device 0-1 binary task offloading is relaxed into a continuous form, with adding an upper bound to guarantee the solution can be as close as possible to the integer. Then introduced by a change of variables in handling the segmented maximum, all non-convex constraints are transformed with slack variables and successive convex approximation. To further ensure the iteration algorithm convergence, the disciplined iteration algorithm is proposed to prevent the iteration from getting stuck. The simulation results verify that the assisted smart user equipment can reduce the system delay combining with the proposed resource allocation algorithm.
Multimode LLC Resonant DC−DC Converters for Wide Range Input Voltage

Conventional resonant DC−DC converters are typically limited to a narrow range of operating input voltage. In this paper, a multimode high-frequency LLC resonant DC−DC converter designed to operate with a wide range of input voltage, is proposed. The converter has three operating modes. It can provide a variable and wide voltage gain conversion via mode switching. Besides, this converter can attain a constant switching frequency operation, as well as ZVS operation for its switches and ZCS operation for its rectifier diodes. A 200 W prototype is constructed. Experiments are conducted to verify its features. Results show that the converter is functional over a wide range of input voltage (25 V-200 V) to provide a fixed output voltage of 200 V, while maintaining the ZVS and ZCS operations and high-efficiency power conversion.
Masked Neural Style Transfer using Convolutional Neural Networks

In painting, humans can draw an interrelation between the style and the content of a given image in order to enhance visual experiences. Deep neural networks like convolutional neural networks are being used to draw a satisfying conclusion of this problem of neural style transfer due to their exceptional results in the key areas of visual perceptions such as object detection and face recognition.In this study, along with style transfer on whole image it is also outlined how transfer of style can be performed only on the specific parts of the content image which is accomplished by using masks. The style is transferred in a way that there is a least amount of loss to the content image i.e., semantics of the image is preserved.
Comparison of Modulation Methods for Visible Light Communication System Using Organic LED

Reported visible light communication systems using organic light-emitting diode focus on two main modulation approaches: single carrier modulation with artificial neural networks and multi-carrier modulation. Due to differences in experimental conditions, there is no direct comparison between the two approaches. In this paper, the best modulation schemes from both approaches were experimentally tested on the same system. Experimental results showed advantages and disadvantages by direct comparison between approaches. Moreover, through rigorous training and testing, experimental results indicated that the same artificial neural networks structure could be re-used to greatly reduce testing time of similar systems. Last but not least, our system employed driving circuit with active equalizer, which expanded system frequency response by 45 times.
Joint Image and 3D Shape Part Representation in Large Collections for Object Blending

We propose a new approach to perform object shape retrieval from images, it can handle the shape of the part of the object and combine parts from different sources to find a different 3D shape. Our method creates a common representation for images and 3D models that enables mixing elements from both kinds of inputs. Our approach automatically extracts the desired part and its 3D shape from each source without the need of annotations. There are many applications to combining parts from images and 3D models, for example, performing smart online catalogue searches by selecting the parts that we are looking for from images or 3D models and retrieve a 3D shape that has the desired arrangement of parts. Our approach is capable of obtaining the shape of the parts of an object from an image in the wild, independently of the pose of the object and without the need of annotations of any kind.
uso de instrumentos derivativos sobre o valor das firmas brasileiras

This paper analyzes the impact of the use of derivatives and the direction and magnitude of the premium coverage related to each type of derivative instrument on firm value for a sample of non-financial Brazilian companies traded in the period from 2004 to 2007. The results indicate that the use of derivatives is related to a significant and positive impact on companies’ market value. The evidence shows that the use of derivatives is associated with a “premium cove-rage”. Thus, it can be concluded that firms adopting risk management practices using derivatives have a higher market value, in the Brazilian market, compa-red to firms that do not use these financial instruments (hedging premium). Results also indicate that, when the analysis focuses on the type of instrument used, the risk management with different types of derivatives instruments gene-• rates an impact of heterogeneous magnitude on firm value. Swap and future/ forward contracts have a positive and statistically significant impact. As for the options, although positive, the impact was not statistically significant. Therefo-re, the results not only confirm the expectation that the use of derivatives has a statistically significant positive effect on firm value, but also show that there is a relevant difference in effects associated with different derivatives types. Toge-ther, the results support the fact that investors are willing to pay a higher value for firms that actively manage financial risk in more volatile environments such as Brazil, and this willingness varies according to the instrument type used and the possible combination of these financial derivatives. In future research, an analysis of the causes of the heterogeneous impact of different derivative instruments should be performed to better understand the mechanisms by which risk management policy creates value for firms.
Part of Speech Tagging for Setswana African Language

Part of speech (POS) tagging is the technique that assigns appropriate lexical categories to words in a sentence. It is a crucial step in Natural Language Processing (NLP) applications such as Machine Translation, Spell and Grammar checking, Word Predictions, Information Retrieval, etc‥ A lot of work has been done on POS tagging mainly for European and Asiatic languages, while in Africa, more work is needed mostly due to the lack of the annotated corpus. Some significant works have been done on African languages, such as Arabic, Igbo, Swahili and Yoruba, South African official languages. However, African languages are generally under-resourced, in particular, in terms of lexical semantics annotated corpora, necessary for effective NLP tools and applications. Hence, advances in this direction have been limited. The main aim of the work reported in this paper is the development of a POS tagger model for an under-resourced Setswana African language. A review of some POS taggers for different African languages is conducted, challenges and techniques used in creating the POS taggers are elicited, and a POS tagger model for Setswana language using SVMTool is presented.
A Maximum Likelihood Approach to Joint Groupwise Image Registration and Fusion by a Student-$t$ Mixture Model

In this paper, we propose a Student-$t$ mixture model (SMM) to approximate the joint intensity scatter plot (JISP) of the groupwise images. The problem of joint groupwise image registration and fusion is considered as a maximum likelihood (ML) formulation. The parameters of registration and fusion are estimated simultaneously by an expectation maximization (EM) algorithm. To evaluate the performance of the proposed method, experiments on several types of multimodal images are performed. Comprehensive experiments demonstrate that the proposed approach has better performance than other methods.
A Sub-Harmonic Injection Phase Locked Loop with Spur Reduction Technique

This letter presents a sub-harmonic injection phase locked loop (SIPLL) and use gain-boosted charge pump structure and a divided by 48 pulse swallow dividers. This SIPLL is fabricated in a 0.18-μm CMOS process. The measured output frequency ranges from 2.1 to 2.4 GHz. Its power is 15 mW for a supply of 1.8 V at 42.4 GHz. The root-mean-square jitter is 15 ps.
A Comparison of Low-frequency Power Distribution on a Realistic Head Model and a Sphere Model

Local field potential(LFP) power is derived from the scalp EEG, whose significant value can reflect the strength of neural signal in the deep brain, thus it has been widely utilized in the analysis of EEG rhythms in the neural system. However, the electrical significance of power hasn't been understood totally to affect the accurate detection on EEG rhythms. Therefore, the sensitivity of low-frequency power(a wide application parameter in the practice) was studied by the forward problem, based on the theory of equivalent dipole current in a generalized expression of EEG. The oscillatory field potentials(FPs, representing for scalp EEG recordings) evoked by a low-frequency dipole current(representing for rhythmic generator in the deep brain, 6Hz sine function here) were estimated by finite element method(FEM) on the surface of a homogeneous one-layer head model, where the power values were estimated by Hilbert transform and indicated by the FPs at the amplitude of dipole current, and the significant power values(LFP SPs, those values beyond mean power indicated by warm color on the standard color-map) were concerned. Because the analytic solution of the FPs on the sphere surface would help to better understand LFP SPs on the surface of a realistic head model, a comparison study was performed. One point to emphasize, we supposed only one computational setting, that, moment direction of dipole current to be different in the study, while the other settings similar in the computing two models. Similarly, it was found that low-frequency LFP SPs were sensitive to multiple factors, such as, brain tissue anisotropy, position of dipole current, and sites of recording electrodes on the head surface. Additionally, the feature of limited space of LFP SP with a great LFP SP level might character the effect of electrical field conduction of brain tissue on the low-frequency power of EEG rhythms. Reversely, it was firstly reported that the effect of anisotropic conductivity of brain tissue on LFP SP could be reversed by the moment orientation of dipole current, implying that the direction of rhythmic generator could be the determinant factor to the influence of anisotropy on the low-frequency power of EEG rhythms on the scalp. The work mapped the relationship between the scalp EEG and the electrical activity in the deep brain, in particular, highlighted the important role of orientation of source current in the mapping. Therefore, it might be helpful to the measurement and analysis of spontaneous low-frequency EEG rhythms.
Probabilistic Study of Software Defects Underlying Relation between Pre-Release and Post-Release Defects

Components that have defects after release, but not during testing, are very undesirable as they point to ‘holes’ in the testing process. In this work, the main objective is to provide a relation between pre-release & post-release defects. This work describes the initial effort of building analysis for defects in system testing carried out by an independent testing team. The motivation to have such correlation analysis in software defects is to serve as an early quality indicator of the software entering system testing and assist the testing team to manage and control test execution activities. Dataset is analysed with curve fitting methods & then sets are validated using correlation methods. After this, different performance parameters are calculated using probabilistic analysis tools.
Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

To cope with the soft sensor modeling of unobserved multimode nonlinear processes, this paper proposes a modified kernel partial least squares (KPLS) by integrating latent factor clustering (LFC), called LFC-KPLS. In the proposed method, the process data are first divided into several batches orderly, and then projected onto the latent space by using the nonlinear functional expansion technology. In the latent space, partial least squares method is applied to compute the regression coefficients between the input variables and output variable of each batch. These regression coefficients, called the latent factors, can describe the functional relationships in the unobserved multimode data. Therefore, the latent factors are used for mode clustering so that the process data with similar functional relations can be clustered in one mode together. For each mode, the nonlinear soft sensor is established based on KPLS. To assign the mode of the online query sample, a mode identification strategy based on Bayesian inference is designed for the soft sensor online prediction. Finally, two cases studies are adopted to validate the proposed method.
QUALITY OF LIFE AT WORK AND WILLINGNESS TOWARD ORGANIZATIONAL CHANGE IN OFFICERS OF COMPANIES IN THE CITY OF BOGOTÁ - COLOMBIA

Resumen El proposito de la investigacion fue determinar la relacion entre la calidad de vida laboral y la disposicion al cambio organizacional en una muestra de 100 funcionarios de empresas de la ciudad de Bogota. Se realizo bajo un diseno transversal correlacional, a traves de la aplicacion de dos instrumentos: el instrumento de medicion de cambio organizacional (IMC) (Garcia & Forero, 2010) y el de calidad de vida percibida en organizaciones (Gomez – Rada, 2011). La participacion de la poblacion fue voluntaria. El analisis de los datos se realizo por medio del Rho de Spearman. Los resultados obtenidos muestran una correlacion significativa al 0.001 con todas las variables de cambio organizacional y de calidad de vida, excepto las de resistencia al cambio. Estos hallazgos sugieren que algunos de los aspectos a resaltar son la importancia de la calidad de vida y el compromiso con las tareas a realizar, ya que estos definen las metas y objetivos planteados por la entidad y enmarcan el trabajo y la productividad de los empleados, al igual que su disposicion al cambio.
Radio Source Localization Using Received Signal Strength in a Multipath Environment

We consider the problem of localizing a radio emitter in a wireless network using RSS measured by a set of known network nodes in a multipath environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is nontrivial in the presence of multipath. We propose a HMM model within a Bayesian learning framework for processing RSS data in the localization process to deal with RSS fluctuations induced by multipath interference. To address the uncertainty of emitter dynamics, a semi-Markov model is also adopted to model the duration time of the emitter sojourn in a state. We compare the performance of the HMM methods, HsMM methods and RSS fingerprinting methods via a real experiment of a two-region emitter localization problem and Monte Carlo simulations using ray-tracing software.
New considerations for travelling wave particle handling

Different small-scale devices for particle micro-handling using an AC electric field boundary wave were proposed by F.M. Moesner et. al. (1995). These devices, with various novel transportation and manipulation features, that instantly generate particle driving forces through electric field creation, have been designed and produced. In a further endeavour, the mechanisms of particle conveyance are subsequently validated in experiments. Particles on a thin protecting and insulating film interface above a series of encased and insulated parallel field electrodes become either tribo-electrically or induction charged through the application of balanced three- or six-phase voltages. The created nonuniform travelling field-wave conveys the charged particles perpendicular to the electrodes by repulsive forces in a hopping mode from electrode to electrode. In a temperature and humidity controlled environment, experiments surveying particle conveyance dynamics on these electric devices have been carried out with help of a high speed camera. The applied experimental voltages included triangular, ramp and two customized profiles. Various particle materials with diameters up to 400 /spl mu/m have been examined; metal, glass, and plastic spheres showed the best performances. The transient charge distribution on particles and on the activated electric device, which is responsible for the particle conveyance, has been inspected by a modified scanning electron microscope (SEM). Further, theoretical considerations underline the experimental findings.
China's Monetary Challenges

Despite the People's Republic of China's remarkable growth over the post-1978 reform period, questions have arisen about the sustainability of its exchange rate policy and the soundness of its financial system. This book focuses on the key monetary challenges to China's continued advancement and addresses such topical issues as the build-up of foreign exchange reserves, monetary control, credit allocation difficulties, and the expanding role of China's asset markets and stock exchanges. Current and past monetary policy strategies are examined in detail as are the banking sector reforms leading up to full foreign competition in December 2006. The analysis also assesses the People's Republic's role within Greater China (including Hong Kong and Taiwan) and the potential for future renminbi monetary hegemony within Asia. The treatment of these issues is intended to be accessible to non-economists and does not assume prior immersion in the underlying formal models.
Acoustic Analysis of the Speakers’ Variability for Regional Accent-Affected Pronunciation in Bangladeshi Bangla: A Study on Sylheti Accent

Accented pronunciation variability is one of the key elements that deteriorate the accuracy of the automatic speech recognition (ASR). This article reports the results of the acoustic analysis of the two groups of speakers’ variability caused by regional accent in Bangladeshi Bangla. The analysis considers the seven monophthongal and four diphthongal vowels of Bangla to investigate the acoustic characteristics of two groups of single-accent speakers and their correlation on the articulation of the Standard Colloquial Bangladeshi Bangla (SCBB). An accent is the speaker’s regional signature and shaped by his/her community and educational background. This study examines both male and female speakers from the Sylhet region, which has one of the extremely deviant dialects in Bangla, and comparatively less deviant speakers from different districts of North-West and Middle Part of Bangladesh. Accent-related acoustic features such as pitch slope, formant frequencies, and vowel duration have been considered to examine the prominent characteristics of the accents and to classify the accents from these features. Both gender groups are distinctly analyzed. It has been found that there are significant deviations in formant frequencies and various steepness of the rise/fall in pitch slope within accents of both gender groups. In this study, it has been observed that accent related changes in speech affect the ASR performance. This has emphasized the need for accent-specific acoustic models to handle the speakers from highly deviant dialects as well as considering the accent-affected speakers’ variability in the corpora development for robust ASR system in Bangladeshi Bangla.