Search results for: measurement accuracy

Authors: M. Abdo, Ahmed Elghandour, Khairy Eltahlawy, Mohamed Shalaby

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A key development in the field of inertial sensors, fiber-optic gyroscopes (FOGs) are currently thought to be a competitive alternative to mechanical gyroscopes for inertial navigation and control applications. For the past few years, research and development efforts have been conducted all around the world using the FOG as a crucial sensor for high-accuracy inertial navigation systems. The main fundamentals of optical gyros were covered in this essay, followed by discussions of the main types of optical gyros—fiber optic gyroscopes and ring laser gyroscopes—and comparisons between them. We also discussed different types of fiber optic gyros, including interferometric, resonator, and brillion fiber optic gyroscopes.

Keywords: mechanical gyros, ring laser gyros, interferometric fiber optic gyros, resonator fiber optic gyros

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Authors: Ekkehard Fehling, Paul Capewell

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In recent years, the importance of masonry glued by polyurethane adhesive has increased. In 2021, the Institute of Structural Engineering of the University of Kassel was commissioned to carry out quasi-static in-plane shear tests on prefabricated brick masonry panel systems with 2K PUR adhesive in order to investigate the load-bearing behavior during earthquakes. In addition to the usual measurement of deformations using displacement transducers, all tests were documented using an optical measuring system (“GOM”), which was used to determine the surface strains and deformations of the test walls. To compare the results with conventional mortar walls, additional reference tests were carried out on test specimens with thin-bed mortar joints. This article summarizes the results of the test program and provides a comparison between the load-bearing behavior of masonry bonded with polyurethane adhesive and thin bed mortar in order to enable realistic non-linear modeling.

Keywords: masonry, shear tests, in-plane, polyurethane adhesive

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Authors: Chun-Ying Lee, Mei-Wen Wu, Li-Yi Cheng, Chiang-Ho Cheng

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This research studies the electroplating of zinc coating in the zinc chloride bath mixed with supercritical CO2. The sodium fluoride (NaF) was used as the bath additive to change the structure and property of the coating, and therefore the roughness and corrosion resistance of the zinc coating was investigated. The surface characterization was performed using optical microscope (OM), X-ray diffractometer (XRD), and α-step profilometer. Moreover, the potentiodynamic polarization measurement in 3% NaCl solution was employed in the corrosion resistance evaluation. Because of the emulsification of the electrolyte mixed in Sc-CO2, the electroplated zinc produced the coating with smoother surface, smaller grain, better throwing power and higher corrosion resistance. The main role played by the NaF was to reduce the coating’s roughness and grain size. In other words, the CO2 mixed with the electrolyte under the supercritical condition performed the similar function as brighter and leveler in zinc electroplating to enhance the throwing power and corrosion resistance of the coating.

Keywords: supercritical CO2, zinc-electroplating, sodium fluoride, electroplating

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Authors: Miroslaw Wendeker, Piotr Kacejko, Mariusz Duk, Pawel Karpinski

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The research in the field of aircraft internal combustion engines is currently driven by the needs of decreasing fuel consumption and CO2 emissions, while fulfilling the level of safety. Currently, reciprocating aircraft engines are found in sports, emergency, agricultural and recreation aviation. Technically, they are most at a pre-war knowledge of the theory of operation, design and manufacturing technology, especially if compared to that high level of development of automotive engines. Typically, these engines are driven by carburetors of a quite primitive construction. At present, due to environmental requirements and dealing with a climate change, it is beneficial to develop aircraft piston engines and adopt the achievements of automotive engineering such as computer-controlled low-pressure injection, electronic ignition control and biofuels. The paper describes simulation research of the innovative power and control systems for the aircraft radial engine of high power. Installing an electronic ignition system in the radial aircraft engine is a fundamental innovative idea of this solution. Consequently, the required level of safety and better functionality as compared to the today’s plug system can be guaranteed. In this framework, this research work focuses on describing a methodology for optimizing the electronically controlled ignition system. This attempt can reduce emissions of toxic compounds as a result of lowered fuel consumption, optimized combustion and engine capability of efficient combustion of ecological fuels. New, redundant elements of the control system can improve the safety of aircraft. Consequently, the required level of safety and better functionality as compared to the today’s plug system can be guaranteed. The simulation research aimed to determine the vulnerability of the values measured (they were planned as the quantities measured by the measurement systems) to determining the optimal ignition angle (the angle of maximum torque at a given operating point). The described results covered: a) research in steady states; b) velocity ranging from 1500 to 2200 rpm (every 100 rpm); c) loading ranging from propeller power to maximum power; d) altitude ranging according to the International Standard Atmosphere from 0 to 8000 m (every 1000 m); e) fuel: automotive gasoline ES95. The three models of different types of ignition coil (different energy discharge) were studied. The analysis aimed at the optimization of the design of the innovative ignition system for an aircraft engine. The optimization involved: a) the optimization of the measurement systems; b) the optimization of actuator systems. The studies enabled the research on the vulnerability of the signals to the control of the ignition timing. Accordingly, the number and type of sensors were determined for the ignition system to achieve its optimal performance. The results confirmed the limited benefits, in terms of fuel consumption. Thus, including spark management in the optimization is mandatory to significantly decrease the fuel consumption. This work has been financed by the Polish National Centre for Research and Development, INNOLOT, under Grant Agreement No. INNOLOT/I/1/NCBR/2013.

Keywords: piston engine, radial engine, ignition system, CFD model, engine optimization

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Authors: C. Sancho Salvatierra, J. M. Martinez Nieto, R. García Gonzalez-Gordon, M. I. Martinez Bellido

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Introduction: Nature could act as an asset for human health protecting against possible diseases and promoting the state of both physical and mental health. Goals: This paper aims to determine which natural elements present evidence that show positive influence on human health, on which particular aspects and how. It also aims to determine the best biomarkers to measure such influence. Method: A systematic literature review was carried out. First, a general free text search was performed in databases, such as Scopus, PubMed or PsychInfo. Secondly, a specific search was performed combining keywords in order of increasing complexity. Also the Snowballing technique was used and it was consulted in the CSIC’s (The Spanish National Research Council). Databases: Of the 130 articles obtained and reviewed, 80 referred to natural elements that influenced health. These 80 articles were classified and tabulated according to the nature elements found, the health aspects studied, the health measurement parameters used and the measurement techniques used. In this classification the results of the studies were codified according to whether they were positive, negative or neutral both for the elements of nature and for the aspects of health studied. Finally, the results of the 80 selected studies were summarized and categorized according to the elements of nature that showed the greatest positive influence on health and the biomarkers that had shown greater reliability to measure said influence. Results: Of the 80 articles studied, 24 (30.0%) were reviews and 56 (70.0%) were original research articles. Among the 24 reviews, 18 (75%) found positive results of natural elements on health, and 6 (25%) both positive and negative effects. Of the 56 original articles, 47 (83.9%) showed positive results, 3 (5.4%) both positive and negative, 4 (7.1%) negative effects, and 2 (3.6%) found no effects. The results reflect positive effects of different elements of nature on the following pathologies: diabetes, high blood pressure, stress, attention deficit hyperactivity disorder, psychotic, anxiety and affective disorders. They also show positive effects on the following areas: immune system, social interaction, recovery after illness, mood, decreased aggressiveness, concentrated attention, cognitive performance, restful sleep, vitality and sense of well-being. Among the elements of nature studied, those that show the greatest positive influence on health are forest immersion, natural views, daylight, outdoor physical activity, active transport, vegetation biodiversity, natural sounds and the green residences. As for the biomarkers used that show greater reliability to measure the effects of natural elements are the levels of cortisol (both in blood and saliva), vitamin D levels, serotonin and melatonin, blood pressure, heart rate, muscle tension and skin conductance. Conclusions: Nature is an asset for health, well-being and quality of life. Awareness programs, education and health promotion are needed based on the elements that nature brings us, which in turn generate proactive attitudes in the population towards the protection and conservation of nature. The studies related to this subject in Spain are very scarce. Aknowledgements. This study has been promoted and partially financed by the Environmental Foundation Jaime González-Gordon.

Keywords: health, green areas, nature, well-being

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Shih-Pin Chen, Shih-Syuan You

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Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming

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Authors: Weizhi Zhong, Xiaoyi Lu, Lei Xu

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In order to solve the problem of inaccurate synchronization for distributed multiple-input multiple-output (MIMO) system in multipath environment, a golay pair aided timing synchronization method is proposed in this paper. A new synchronous training sequence based on golay pair is designed. By utilizing the aperiodic auto-correlation complementary property of the new training sequence, the fine timing point is obtained at the receiver. Simulation results show that, compared with the tradition timing synchronization approaches, the proposed algorithm can provide high accuracy in synchronization, especially under multipath condition.

Keywords: distributed MIMO system, golay pair, multipath, synchronization

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Authors: Shoji Mori, Kunito Okuyama

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Annular two phase flow is encountered in many industrial equipments, including flow near nuclear fuel rods in boiling water reactor (BWR). Especially, disturbance waves play important roles in the pressure drop, the generation of entrainments, and the dryout of the liquid film. Therefore, it is important to clarify the behavior of disturbance waves and base film. However, most of the previous studies have been performed under atmospheric pressure conditions that provides the properties of liquid and gas which are significantly different from those of a BWR. Therefore, the effect of properties in gas and liquid on liquid film characteristics should be clarified. In this paper we focus on the effect of gas-liquid density ratio on liquid film thickness characteristics. The experiments have been conducted at four density ratio conditions (ρL/ρG =763, 451, 231, and 31). As a result, it is found that and interfacial shear stress collapse not only tF ave but also tF max and tF min successfully under the same liquid mass flow rate conditions irrespective of ρL/ρG, and moreover a non-dimensional parameter tends to collapse tF max，tF ave，and tF min in the wide range of experimental conditions (ρL/ρG：31～763，We：10～1800，ReL:500 ～ 2200).

Keywords: two phase flow, liquid film, annular flow, disturbance wave

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

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Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

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Authors: P. Tueku, P. Supnithi, R. Wongsathan

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Heat-Assisted Magnetic Recording (HAMR) is one of the leading technologies identified to enable areal density beyond 1 Tb/in2 of magnetic recording systems. A key challenge to HAMR designing is accuracy of positioning, timing of the firing laser, power of the laser, thermo-magnetic head, head-disk interface and cooling system. We study the effect of HAMR parameters on transition center and transition width. The HAMR is model using Thermal Williams-Comstock (TWC) and microtrack model. The target and equalizer are designed by the minimum mean square error (MMSE). The result shows that the unit energy constraint outperforms other constraints.

Keywords: heat-assisted magnetic recording, thermal Williams-Comstock equation, microtrack model, equalizer

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Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

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Authors: Refilwe Moeletsi

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Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

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Authors: Jibon Sharma, Alakjyoti Patowary, Moirangthem Nara Singh

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In experimental research it is very much essential to obtain the quality control of the system used for the experiment. NaI (Tl) scintillation detector is the most commonly used in radiation and medical physics for measurement of the gamma ray activity of various samples. In addition, the scintillation detector has a lot of applications in the elemental analysis of various compounds, alloys using activation analysis. In each application for quantitative analysis, it is very much essential to know the detection efficiency and resolution for different gamma energies. In this work, the energy dependence of efficiency and resolution of NaI (Tl) detector using gamma-ray spectroscopy are investigated. Different photon energies of 356.01 keV,511keV,661.60keV,1170 keV,1274.53 keV and 1330 keV are obtained from four radioactive sources (133Ba,22Na,137Cs and 60 Co) used in these studies. Values of full energy peak efficiencies of these gamma energies are found to be respectively 58.46%,10.15%,14.39%,1.4%,3.27% and 1.31%. The values of percent resolution for above different gamma ray energies are found to be 11.27%,7.27%,6.38%,5.17%,4.86% and 4.74% respectively. It was found that the efficiency of the detector exponentially decreases with energy and the resolution of the detector is directly proportional to the energy of gamma-ray.

Keywords: naI (Tl) gamma-ray spectrometer, resolution, full energy peak efficiency, radioactive sources

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Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

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Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of the biomass platform, comprising a potential pool of hydrogen energy that stands as a distinct energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need for in-situ H₂ production, which plays a key role in the hydrogenation reactions of biomass into higher-value components. It is reported elsewhere in the literature that catalytic decomposition of FA is usually performed in poorly designed setups using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. Our work suggests an approach that integrates designing a distinct catalyst featuring magnetic properties with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for the dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H₂ gas from FA. Using an ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under an inert medium. Through a distinct approach, FA is charged into the reactor via a high-pressure positive displacement pump at steady-state conditions. The produced gas (H₂+CO₂) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The uniqueness of this work lies in designing a very responsive catalyst, pumping a consistent amount of FA into a sealed reactor running at steady-state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at a lower temperature range (35-50°C) yielded more gas, while the catalyst loading and Pd doping wt.% were found to be the most significant factors with P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

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Authors: Ko-Shao Chen, Yun Tsao, Chia-Hsuan Tsen, Chien-Chung Chen, Shu-Chuan Liao

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Electrospun microtube array membranes (MTAMs) made of PLLA (poly-L-lactic acid) have wide potential applications in tissue engineering. However, their surface hydrophobicity and poor biocompatability have limited their further usage. In this study, the surface of PLLA MTAMs were made hydrophilic by introducing extra functional groups, such as peroxide, via an acetic acid plasma (AAP). UV-graft polymerization of acrylic acid (G-AAc) was then used to produce carboxyl group on MTAMs surface, which bonded covalently with chitosan through EDC / NHS crosslinking agents. To evaluate the effects of the surface modification on PLLA MTAMs, water contact angle (WCA) measurement and cell compatibility tests were carried out. We found that AAP treated electrospun PLLA MTAMs grafted with AAc and, finally, with chitosan immobilized via crosslinking agent, exhibited improved hydrophilic and cell compatibility.

Keywords: plasma, EDC/NHS, UV grafting, Chitosan, microtube array membrane (MTAMs)

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Authors: Tahira Pirzada, Zahra Ashrafi, Saad Khan

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We present a facile and sustainable solid templating approach to fabricate highly porous, flexible and superhydrophobic aerogels of composite nanofibers of cellulose diacetate and silica which are produced through sol gel electrospinning. Scanning electron microscopy, contact angle measurement, and attenuated total reflection-Fourier transform infrared spectrometry are used to understand the structural features of the resultant aerogels while thermogravimetric analysis and differential scanning calorimetry demonstrate their thermal stability. These aerogels exhibit a self-supportive three-dimensional network abundant in large secondary pores surrounded by primary pores resulting in a highly porous structure. Thermal crosslinking of the aerogels has further stabilized their structure and flexibility without compromising on the porosity. Ease of processing, thermal stability, high porosity and oleophilic nature of these aerogels make them promising candidate for a wide variety of applications including acoustic and thermal insulation and oil and water separation.

Keywords: hybrid aerogels, sol-gel electrospinning, oil-water separation, nanofibers

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Authors: A. Melouah, M. Diaf

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The III-N nitride semiconductors appear to be excellent host materials, in particular, GaN epilayers doped with Erbium ions have shown a highly reduced thermal quenching of the Er luminescence intensity from cryogenic to elevated temperatures. The remarkable stability may be due to the large energy band gap of the material. Two methods are used for doping the Gallium nitride films with Erbium ions; ion implantation in the wafers obtained by (CVDOM) and in-situ incorporation during epitaxial growth of the layers by (MBE). Photoluminescence (PL) spectroscopy has been the main optical technique used to characterize the emission of Er-doped III-N semiconductor materials. This technique involves optical excitation of Er3+ ions and measurement of the spectrum of the light emission as a function of energy (wavelength). Excitation at above band gap energy leads to the creation of Electron-Hole pairs. Some of this pairs may transfer their energy to the Er3+ ions, exciting the 4f-electrons and resulting in optical emission. This corresponds to an indirect excitation of the Er3+ ions by electron-hole pairs. The direct excitation by the optical pumping of the radiation can be obtained.

Keywords: photoluminescence, Erbium, GaN, semiconductor materials

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Authors: Mahbube Eghtesad

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For a number of years, the problem of simultaneous detection and tracking of a target has been one of the most relevant and challenging issues in a wide variety of military and civilian systems. We develop methods for detecting and tracking a target using an orthogonal frequency division multiplexing (OFDM) based radar. As a preliminary step we introduce the target trajectory and Gaussian noise model in discrete time form. Then resorting to match filter and Kalman filter we derive a detector and target tracker. After that we propose an OFDM radar in order to achieve further improvement in tracking performance. The motivation for employing multiple frequencies is that the different scattering centers of a target resonate differently at each frequency. Numerical examples illustrate our analytical results, demonstrating the achieved performance improvement due to the OFDM signaling method.

Keywords: matched filter, target trashing, OFDM radar, Kalman filter

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Authors: Haziyah Hussin

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Hazi Attire software is purposely designed to be used for pattern drafting of the Malay Traditional Women Apparel. It is software created using LISP Program that works under AutoCAD engine and able to draft various patterns for Malay women apparels from fitted, semi-fitted and loose silhouettes. It is fully automatic and the user can select styles from the menu on the screen and enter the measurements. Within five seconds patterns are ready to be printed and sewn. Hazi Attire is different from other programmes available in the market since it is fully automatic, user-friendly and able to print selected pattern chosen quickly and accurately. With this software (Hazi Attire), the selected styles can be generated the pattern according to made-to-measure or standard sizes. It would benefit the apparel industries by reducing manufacturing lead time and cycle time.

Keywords: basic pattern, pattern drafting, toile, Malay traditional women apparel, the measurement parameters, fitted, semi-fitted and loose silhouette

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Authors: Laboratory of Semiconducting, Metallic Materials (LMSM) Biskra Algeria

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InZnSnO2 (IZTO)/β-Ga2O3 Schottky solar barrier photodetector (PhD) exposed to 255 nm was simulated and compared to the measurement. Numerical simulations successfully reproduced the photocurrent at reverse bias and response by taking into account several factors, such as conduction mechanisms and material parameters. By adopting reducing the density of the trap as an improvement. The effect of reducing the bulk trap densities on the photocurrent, response, and time-dependent (continuous conductivity) was studied. As the trap density decreased, the photocurrent increased. The response was 0.04 A/W for the low Ga2O3 trap density. The estimated decay time for the lowest intensity ET (0.74, 1.04 eV) is 0.05 s and is shorter at ∼0.015 s for ET (0.55 eV). This indicates that the shallow traps had the dominant effect (ET = 0.55 eV) on the continuous photoconductivity phenomenon. Furthermore, with decreasing trap densities, this PhD can be considered as a self-powered solar-blind photodiode (SBPhD).

Keywords: IZTO/β-Ga2O3, self-powered solar-blind photodetector, numerical simulation, bulk traps

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Sudhakar Kadiyala, Sripad R. Naik

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Large cavern in Bhutan Himalayas is being monitored since the construction period. The behavior of the cavern is being monitored for last 16 years. Instrumentation includes measurement of convergence of high walls by geodetic monitoring, load on the support systems with load cells and instrumented bolts. Analysis of the results of instrumentation showed that during the construction period of the cavern, the convergence of the cavern varied from 181 - 233 mm in the unit bay area with maximum convergence rate of 2.80mm/day. Whereas during the operational period the total convergence observed was in the range of 21 to 45 mm during a period of 11.30 years with convergence rate of 0.005 to 0.011 mm/day. During the last five years, there were no instances of high tensile stress recorded by the instrumented bolts. Load on the rock bolts have shown stabilization trend at most of the locations. This paper discusses in detail the results of long-term monitoring using the geotechnical instruments and how the data is being used in 3D numerical model to confirm the stability of the cavern.

Keywords: convergence, displacements, geodetic monitoring, long-term stability

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Authors: Rashidah Ghazali, Herlina Abdul Rahim, Mashitah Shikh Maidin, Shafishuhaza Sahlan, Noramli Abdul Razak

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Sound exposure effects have been investigated by broadcasting a group of broilers with sound of Quran verses (Group B) whereas the other group is the control broilers (Group C). The growth rate comparisons in terms of weight and raw meat texture measured by shear force have been investigated. Twenty-seven broilers were randomly selected from each group on Day 24 and weight measurement was carried out every week till the harvest day (Day 39). Group B showed a higher mean weight on Day 24 (1.441±0.013 kg) than Group C. Significant difference in the weight on Day 39 existed for Group B compared to Group C (p< 0.05). However, there was no significant (p> 0.05) difference of shear force in the same muscles (breast and drumstick raw meat) of both groups but the shear force of the breast meat for Group B and C broilers was lower (p < 0.05) than that of their drumstick meat. Thus, broadcasting the sound of Quran verses in the coop can be applied to improve the growth rate of broilers for producing better quality poultry.

Keywords: broilers, sound, shear force, weight

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Authors: Jiyoung Bae, Gyoomi Kim

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This study explores a range of linguistic features used in Korean college students’ argumentative writings for the purpose of developing a model that identifies variables which predict writing proficiencies. This study investigated the latent variable structure of L2 linguistic features, including syntactic complexity, the lexical complexity, and fluency. One hundred forty-six university students in Korea participated in this study. The results of the study’s confirmatory factor analysis (CFA) showed that indicators of linguistic features from this study-provided a foundation for re-categorizing indicators found in extant research on L2 Korean writers depending on each latent variable of linguistic features. The CFA models indicated one measurement model of L2 syntactic complexity and L2 learners’ writing proficiency; these two latent factors were correlated with each other. Based on the overall findings of the study, integrated linguistic features of L2 writings suggested some pedagogical implications in L2 writing instructions.

Keywords: linguistic features, syntactic complexity, lexical complexity, fluency

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

Procedia PDF Downloads 253

Authors: William R. Crawley

Abstract:

The current paper provides an examination of the primary conceptual and historical foundations leading to contemporary perspectives in criminological theory. This subject area involves the examination of theory that is vast and highly interdisciplinary but must, at its core, consider several postulates. The following areas of consideration will be the focus of this examination: presentation of various definitions of criminology as a discipline and attention to a dialogue which inquires as to whether criminological modes of explanation can be regarded as scientific with respect to focus, methods, and findings – e.g., conceptualization, operationalization, measurement strategies, analytical techniques, etc. Specifically, two opposing philosophical frameworks—naturalistic and anti-naturalistic philosophy—are examined by means of conceptual analysis for their necessary and sufficient conditions. Like all academic disciplines, for practitioners and students of criminology to understand and effectively use insights and discoveries, it is imperative that disciplinary axioms and methodologies are critically scrutinized. This paper provides a primer to this critique.

Keywords: anti-naturalistic philosophy, humanistic tradition, is criminology a science, naturalistic philosophy, nomological-deductive model

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Authors: Faisal Aburub, Wael Hadi

Abstract:

Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

Procedia PDF Downloads 281