Modeling And Simulation Of Micro-Manipulator Robotic System For Neurosurgery.

This research focuses on modeling and control simulation of a micro-manipulator robotic system model for neurosurgery application

N-(4-Chloro­butano­yl)-N′-(2,5-dimeth­oxy­phen­yl)thio­urea

The title mol­ecule, C13H17ClN2O3S, shows an anti and syn disposition of the butanoyl and 2,5-dimethoxyphenyl groups with respect to the thione and is stabilized by intra­molecular N—H⋯O and weak C—H⋯S hydrogen bonds. In the crystal, inter­molecular N—H⋯S hydrogen bonds link the mol­ecules into centrosymmetric dimers. The crystal structure is stabilized by weak C—H⋯O and C—H⋯S contacts

Morpheme {BuN-}: an Example of Morphological Process Through Affixation in Bidayuh-Somu Language

The goals of this research were to identify the allomorph of morpheme {buN-}, describe its affixation process, and determine the function of the allomorphs. The qualitative method was applied to gather the data from an informant who was done by recording technique on the field research. The informant was a Bidayuh-Somu language native speaker. The collected data included the derivational words derived from free morpheme that was root, base, and bound morpheme. The free and bound morpheme were then sorted into nominal and verbal class, as well as described qualitatively. Affixation as one of the morphological processes to derive complex derivational word in Bidayuh-Somu Language involved free and bound morpheme. Morpheme {buN-} was chosen as an example to describe the process of affixation in deriving complex derivational word of the language. It is found that the morpheme {buN-} is a prefix and verbal, affixed to the verb, and noun and adjective. Therefore, it functions both as class-maintaining and class-changing. Moreover, it is identified that the morpheme {buN-} has five allomorphs, namely /bu-/ with its alternant /bur-/ and /b-/, and /bum-/, /bun-/, /buN-/ and /bu-/. The morpheme {buN-} is affixed to free morpheme which is initiated with all consonant and vowel phonemes. The morpheme {buN-}also bears meaning

2-Methyl-N-[(3-methyl-2-pyrid­yl)carbamothio­yl]benzamide

In the title compound, C15H15N3OS, the thio­urea group is stabilized by an intra­molecular hydrogen bond between the carbonyl O atom and the thio­amide group. A C—H⋯N intramolecular hydrogen bond is also present. Mol­ecules are linked by inter­molecular N—H⋯O and C—H⋯S hydrogen bonds

Formulation of linguistic regression model based on natural words

When human experts express their ideas and thoughts, human words are basically employed in these expressions. That is, the experts with much professional experiences are capable of making assessment using their intuition and experiences. The measurements and interpretation of characteristics are taken with uncertainty, because most measured characteristics, analytical result, and field data can be interpreted only intuitively by experts. In such cases, judgments may be expressed using linguistic terms by experts. The difficulty in the direct measurement of certain characteristics makes the estimation of these characteristics imprecise. Such measurements may be dealt with the use of fuzzy set theory. As Professor L. A. Zadeh has placed the stress on the importance of the computation with words, fuzzy sets can take a central role in handling words [12, 13]. In this perspective fuzzy logic approach is offten thought as the main and only useful tool to deal with human words. In this paper we intend to present another approach to handle human words instead of fuzzy reasoning. That is, fuzzy regression analysis enables us treat the computation with words. In order to process linguistic variables, we define the vocabulary translation and vocabulary matching which convert linguistic expressions into membership functions on the interval [0–1] on the basis of a linguistic dictionary, and vice versa. We employ fuzzy regression analysis in order to deal with the assessment process of experts from linguistic variables of features and characteristics of an objective into the linguistic expression of the total assessment. The presented process consists of four portions: (1) vocabulary translation, (2) estimation, (3) vocabulary matching and (4) dictionary. We employed fuzzy quantification theory type 2 for estimating the total assessment in terms of linguistic structural attributes which are obtained from an expert

Halalnet: A Deep Neural Network That Classifies the Halalness of Slaughtered Chicken from Their Images

Halal requirement in food is important for millions of Muslims worldwide especially for meat and chicken products, insuring that slaughter houses adhere to this requirement is a challenging task to do manually. In this paper a method is proposed that uses a camera that takes images of slaughtered chicken on the conveyor in a slaughter house, the images are then analyzed by a deep neural network to classify if the image is of a halal slaughtered chicken or not. However, traditional deep learning models require large amounts of data to train on, which in this case these amounts of data were challenging to collect especially the images of non-halal slaughtered chicken, hence this paper shows how the use of one shot learning (Lake, Brenden, Salakhutdinov, Ruslan, Gross & Jas, 2011) and transfer learning (Yosinski, Clune, Bengio & Lipson, 2014) can reach high accuracy on the few amounts of data that were available. The architecture used is based on the Siamese neural networks architecture which ranks the similarity between two inputs (Koch, Zemel & Salakhutdinov, 2015) while using the Xception network (Chollet, 2017) as the twin networks. We call it HalalNet. This work was done as part of SYCUT (syriah compliant slaughtering system) which is a monitoring system that monitors the halalness of the slaughtered chicken in a slaughter house. The data used to train and validate HalalNet was collected from the Azain slaughtering site (Semenyih, Selangor, Malaysia) containing images of both halal and non-halal slaughtered chicken

Alert Correlation Technique Analysis For Diverse Log

Alert correlation is a process that analyses the alerts produced by one or more diverse devices and provides a more succinct and high-level view of occurring or attempted intrusions. The objective of this study is to analyse the current alert correlation technique and identify the significant criteria in each technique that can improve the Intrusion Detection System IDS) problem such as prone to alert flooding, contextual problem, false alert and scalability. The existing alert correlation techniques had been reviewed and analysed. From the analysis, six capability criteria have been identified to improve the current alert correlation techniques which are capability to do alert reduction, alert clustering, identify multi-step attack,reduce false alert, detect known attack and detect unknown attack and technique’s combination is proposed

Mapping the daily rainfall over an ungauged tropical micro-watershed: a downscaling algorithm using gpm data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100-1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds