Search results for: loopback detection

Authors: Ece Cigdem Mutlu, Burak Alakent

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Maintaining the quality of manufactured products at a desired level depends on the stability of process dispersion and location parameters and detection of perturbations in these parameters as promptly as possible. Shewhart control chart is the most widely used technique in statistical process monitoring to monitor the quality of products and control process mean and variability. In the application of Xbar control charts, sample standard deviation and sample mean are known to be the most efficient conventional estimators in determining process dispersion and location parameters, respectively, based on the assumption of independent and normally distributed datasets. On the other hand, there is no guarantee that the real-world data would be normally distributed. In the cases of estimated process parameters from Phase I data clouded with outliers, efficiency of traditional estimators is significantly reduced, and performance of Xbar charts are undesirably low, e.g. occasional outliers in the rational subgroups in Phase I data set may considerably affect the sample mean and standard deviation, resulting a serious delay in detection of inferior products in Phase II. For more efficient application of control charts, it is required to use robust estimators against contaminations, which may exist in Phase I. In the current study, we present a simple approach to construct robust Xbar control charts using average distance to the median, Qn-estimator of scale, M-estimator of scale with logistic psi-function in the estimation of process dispersion parameter, and Harrell-Davis qth quantile estimator, Hodge-Lehmann estimator and M-estimator of location with Huber psi-function and logistic psi-function in the estimation of process location parameter. Phase I efficiency of proposed estimators and Phase II performance of Xbar charts constructed from these estimators are compared with the conventional mean and standard deviation statistics both under normality and against diffuse-localized and symmetric-asymmetric contaminations using 50,000 Monte Carlo simulations on MATLAB. Consequently, it is found that robust estimators yield parameter estimates with higher efficiency against all types of contaminations, and Xbar charts constructed using robust estimators have higher power in detecting disturbances, compared to conventional methods. Additionally, utilizing individuals charts to screen outlier subgroups and employing different combination of dispersion and location estimators on subgroups and individual observations are found to improve the performance of Xbar charts.

Keywords: average run length, M-estimators, quality control, robust estimators

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Authors: Ravi Kumar, Dhrubajit Barman, Nomi Baruah

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Software Cloning has grown an active area in software engineering research community yielding numerous techniques, various tools and other methods for clone detection and removal. The copying, modifying a block of code is identified as cloning as it is the most basic means of software reuse. Agile Software Development is an approach which is currently being used in various software projects, so that it helps to respond the unpredictability of building software through incremental, iterative, work cadences. Software Cloning has been introduced to Agile Environment and many Agile Software Development approaches are using the concept of Software Cloning. This paper discusses the various Agile Software Development approaches. It also discusses the degree to which the Software Cloning concept is being introduced in the Agile Software Development approaches.

Keywords: agile environment, refactoring, reuse, software cloning

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Authors: Sasidhar B., Bhaskar Rao N., Ramesh Babu D. R., Ravi Shankar M.

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Segmentation of lung pleura is a preprocessing step in Computer-Aided Diagnosis (CAD) which helps in reducing false positives in detection of lung cancer. The existing methods fail in extraction of lung regions with the nodules at the pleura of the lungs. In this paper, a new method is proposed which segments lung regions with nodules at the pleura of the lungs based on curvature analysis and morphological operators. The proposed algorithm is tested on 06 patient’s dataset which consists of 60 images of Lung Image Database Consortium (LIDC) and the results are found to be satisfactory with 98.3% average overlap measure (AΩ).

Keywords: curvature analysis, image segmentation, morphological operators, thresholding

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Authors: Nanditha Rajamani, Anirudhaa R. Rao, Divya Sriram

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One of the greatest difficulties in treating patients with pain is the highly subjective nature of pain sensation. The measurement of pain intensity is primarily dependent on the patient’s report, often with little physical evidence to provide objective corroboration. This is also complicated by the fact that there are only few and expensive existing technologies (Functional Magnetic Resonance Imaging-fMRI). The need is thus clear and urgent for a reliable, non-invasive, non-painful, objective, readily adoptable, and coefficient diagnostic platform that provides additional diagnostic information to supplement its current regime with more information to assist doctors in diagnosing these patients. Thus, our idea of developing a pain detector was conceived to take a step further the detection and diagnosis of chronic and acute pain.

Keywords: pain sensor, microwave radiometery, pain sensation, fMRI

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Authors: Steve H. Y. Lee, Jeremy P. E. Spencer

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The aim of the present study was to explore the feasibility of enriching polyphenols in cow milk via addition of flavanone-rich citrus pulp to existing animal feed. 8 Holstein lactating cows were enrolled onto the 4 week feeding study. 4 cows were fed the standard farm diet (control group), with another 4 (treatment group) which are fed a standard farm diet mixed with citrus pulp diet. Milk was collected twice a day, 3 times a week. The resulting milk yield and its macronutrient composition as well as lactose content were measured. The milk phenolic compounds were analysed using electrochemical detection (ECD).

Keywords: milk, polyphenol, animal feeding, lactating cows

Procedia PDF Downloads 298

Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Rajeev Jain, Nimisha Jadon, Kshiti Singh

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Titanium oxide nanoparticles and 1-butyl-2,3-dimethylimidazolium bis (trifluoromethane- sulfonyl) imide modified glassy carbon electrode (TiO2/IL/GCE) has been fabricated for electrochemical sensing of flunarizine dihydrochloride (FRH). The electrochemical properties and morphology of the prepared nanocomposite were studied by electrochemical impedance spectroscopy (EIS) and transmission electron microscopy (TEM). The response of the electrochemical sensor was found to be proportional to the concentrations of FRH in the range from 0.5 µg mL-1 to 16 µg mL-1. The detection limit obtained was 0.03 µg mL-1. The proposed method was also applied to the determination of FRH in pharmaceutical formulation and human serum with good recoveries.

Keywords: flunarizine dihydrochloride, ionic liquid, nanoparticles, voltammetry, human serum

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Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

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Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

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Authors: Krisala Joshi, Sayanta Ghosh, Renu Lata, Jagdish C. Kuniyal

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Throughout the world, monitoring and estimating the changing pattern of forests across diverse landscapes through remote sensing is instrumental in understanding the interactions of human activities and the ecological environment with the changing climate. Forest change detection using satellite imageries has emerged as an important means to gather information on a regional scale. Kullu valley in Himachal Pradesh, India is situated in a transitional zone between the lesser and the greater Himalayas. Thus, it presents a typical rugged mountainous terrain with moderate to high altitude which varies from 1200 meters to over 6000 meters. Due to changes in agricultural cropping patterns, urbanization, industrialization, hydropower generation, climate change, tourism, and anthropogenic forest fire, it has undergone a tremendous transformation in forest cover in the past three decades. The loss and degradation of forest cover results in soil erosion, loss of biodiversity including damage to wildlife habitats, and degradation of watershed areas, and deterioration of the overall quality of nature and life. The supervised classification of LANDSAT satellite data was performed to assess the changes in forest cover in Kullu valley over the years 2000 to 2020. Normalized Burn Ratio (NBR) was calculated to discriminate between burned and unburned areas of the forest. Our study reveals that in Kullu valley, the increasing number of forest fire incidents specifically, those due to anthropogenic activities has been on a rise, each subsequent year. The main objective of the present study is, therefore, to estimate the change in the forest cover of Kullu valley and to address the various social aspects responsible for the anthropogenic forest fires. Also, to assess its impact on the significant changes in the regional climatic factors, specifically, temperature, humidity, and precipitation over three decades, with the help of satellite imageries and ground data. The main outcome of the paper, we believe, will be helpful for the administration for making a quantitative assessment of the forest cover area changes due to anthropogenic activities and devising long-term measures for creating awareness among the local people of the area.

Keywords: Anthropogenic Activities, Forest Change Detection, Normalized Burn Ratio (NBR), Supervised Classification

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Authors: Mohammadamin Abbasnejad

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The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works have been done during the past few years which has their own advantages and disadvantages. In this work, we present a new model based on Gaussian Conditional Random Field. We solve our objective problem using ADMM and we show how well the proposed model works. We train and test our work on two facial expression datasets, CK+, and RU-FACS. Experimental evaluation shows that our proposed approach outperform state of the art expression recognition.

Keywords: Gaussian Conditional Random Field, ADMM, convergence, gradient descent

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Authors: Chee Sun Won

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This paper presents the size reduction of images for machine-to-machine communications. Here, the salient image regions to be preserved include the image patches of the key-points such as corners and blobs. Based on a saliency image map from the key-points and their image patches, an axis-aligned grid-size optimization is proposed for the reduction of image size. To increase the size-reduction efficiency the aspect ratio constraint is relaxed in the constraint optimization framework. The proposed method yields higher matching accuracy after the size reduction than the conventional content-aware image size-reduction methods.

Keywords: image compression, image matching, key-point detection and description, machine-to-machine communication

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Authors: Serpil Kilic, Ihsan Burak Cam, Murat Kilic, Timur Tongur

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Interest in the lead (Pb) has considerably increased due to knowledge about the potential toxic effects of this element, recently. Exposure to heavy metals above the acceptable limit affects human health. Indeed, Pb is accumulated through food chains up to toxic concentrations; therefore, it can pose an adverse potential threat to human health. A sensitive and reliable method for determination of Pb element in red pepper were improved in the present study. Samples (33 red pepper products having different brands) were purchased from different markets in Turkey. The selected method validation criteria (linearity, Limit of Detection, Limit of Quantification, recovery, and trueness) demonstrated. Recovery values close to 100% showed adequate precision and accuracy for analysis. According to the results of red pepper analysis, all of the tested lead element in the samples was determined at various concentrations. A Perkin- Elmer ELAN DRC-e model ICP-MS system was used for detection of Pb. Organic red pepper was used to obtain a matrix for all method validation studies. The certified reference material, Fapas chili powder, was digested and analyzed, together with the different sample batches. Three replicates from each sample were digested and analyzed. The results of the exposure levels of the elements were discussed considering the scientific opinions of the European Food Safety Authority (EFSA), which is the European Union’s (EU) risk assessment source associated with food safety. The Target Hazard Quotient (THQ) was described by the United States Environmental Protection Agency (USEPA) for the calculation of potential health risks associated with long-term exposure to chemical pollutants. THQ value contains intake of elements, exposure frequency and duration, body weight and the oral reference dose (RfD). If the THQ value is lower than one, it means that the exposed population is assumed to be safe and 1 < THQ < 5 means that the exposed population is in a level of concern interval. In this study, the THQ of Pb was obtained as < 1. The results of THQ calculations showed that the values were below one for all the tested, meaning the samples did not pose a health risk to the local population. This work was supported by The Scientific Research Projects Coordination Unit of Akdeniz University. Project Number: FBA-2017-2494.

Keywords: lead analyses, red pepper, risk assessment, daily exposure

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Authors: Kuniaki Kudo, Raviraj Solanki, Kaushal Patel, Yash Virani

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This research mainly concerns optimizing UI test automation for large-scale web applications. The test target application is the HHAexchange homecare management WEB application that seamlessly connects providers, state Medicaid programs, managed care organizations (MCOs), and caregivers through one platform with large-scale functionalities. This study focuses on user interface automation testing for the WEB application. The quality assurance team must execute many manual users interface test cases in the development process to confirm no regression bugs. The team automated 346 test cases; the UI automation test execution time was over 17 hours. The business requirement was reducing the execution time to release high-quality products quickly, and the quality assurance automation team modernized the test automation framework to optimize the execution time. The base of the WEB UI automation test environment is Selenium, and the test code is written in Python. Adopting a compilation language to write test code leads to an inefficient flow when introducing scalability into a traditional test automation environment. In order to efficiently introduce scalability into Test Automation, a scripting language was adopted. The scalability implementation is mainly implemented with AWS's serverless technology, an elastic container service. The definition of scalability here is the ability to automatically set up computers to test automation and increase or decrease the number of computers running those tests. This means the scalable mechanism can help test cases run parallelly. Then test execution time is dramatically decreased. Also, introducing scalable test automation is for more than just reducing test execution time. There is a possibility that some challenging bugs are detected by introducing scalable test automation, such as race conditions, Etc. since test cases can be executed at same timing. If API and Unit tests are implemented, the test strategies can be adopted more efficiently for this scalability testing. However, in WEB applications, as a practical matter, API and Unit testing cannot cover 100% functional testing since they do not reach front-end codes. This study applied a scalable UI automation testing strategy to the large-scale homecare management system. It confirmed the optimization of the test case execution time and the detection of a challenging bug. This study first describes the detailed architecture of the scalable test automation environment, then describes the actual performance reduction time and an example of challenging issue detection.

Keywords: aws, elastic container service, scalability, serverless, ui automation test

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Authors: Golnaz Shahtahmassebi, Jose Maria Sarabia

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In this talk, we introduce a new class of conjugate prior distributions obtained from conditional specification methodology. We illustrate the application of such distribution in Bayesian change point detection in Poisson processes. We obtain the posterior distribution of model parameters using a general bivariate distribution with gamma conditionals. Simulation from the posterior is readily implemented using a Gibbs sampling algorithm. The Gibbs sampling is implemented even when using conditional densities that are incompatible or only compatible with an improper joint density. The application of such methods will be demonstrated using examples of simulated and real data.

Keywords: change point, bayesian inference, Gibbs sampler, conditional specification, gamma conditional distributions

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Authors: Kinjal Parmar, Matteo Lelli

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The usage of IoT technology as a rockfall alert system is relatively new. This paper explains the potential of such an alert system called HelloMac from Maccaferri which provides transportation infrastructure asset owners the way to effectively utilize their resources in the detection of boulder impacts on rockfall barriers. This would ensure a faster assessment of the impacted barrier and subsequently facilitates the implementation of remedial works in an effective and timely manner. In addition, the HelloMac can also be integrated with another warning system to alert vehicle users of the unseen dangers ahead. HelloMac is developed to work also in remote areas, where cell coverage is not available. User gets notified when a rockfall even occurs via mobile app, SMS and email. Using such alarming systems effectively, we can reduce the risk of rockfall hazard.

Keywords: rockfall, barrier, HelloMac, rockfall alert system

Procedia PDF Downloads 52

Authors: O. Tovide, N. Jahed, N. Mohammed, C. E. Sunday, H. R. Makelane, R. F. Ajayi, K. M. Molapo, A. Tsegaye, M. Masikini, S. Mailu, A. Baleg, T. Waryo, P. G. Baker, E. I. Iwuoha

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A graphenated–polyaniline (GR-PANI) nanocomposite sensor was constructed and used for the determination of anthracene. The direct electro-oxidation behavior of anthracene on the GR-PANI modified glassy carbon electrode (GCE) was used as the sensing principle. The results indicate thatthe response profile of the oxidation of anthracene on GR-PANI-modified GCE provides for the construction of sensor systems based onamperometric and potentiometric signal transductions. A dynamic linear range of 0.12- 100 µM anthracene and a detection limit of 0.044 µM anthracene were established for the sensor system.

Keywords: electrochemical sensors, environmental pollutants, graphenated-polymers, polyaromatic hydrocarbon

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Authors: M. Beheshti, S. Saegrov, T. M. Muthanna

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Separated sewer systems are designed to transfer the wastewater from houses and industrial sections to wastewater treatment plants. Unwanted water in the sewer systems is a well-known problem, i.e. storm-water inflow is around 50% of the foul sewer, and groundwater infiltration to the sewer system can exceed 50% of total wastewater volume in deteriorated networks. Infiltration and inflow of non-sewer water (I/I) into sewer systems is unfavorable in separated sewer systems and can trigger overloading the system and reducing the efficiency of wastewater treatment plants. Moreover, I/I has negative economic, environmental, and social impacts on urban areas. Therefore, for having sustainable management of urban sewer systems, I/I of unwanted water into the urban sewer systems should be considered carefully and maintenance and rehabilitation plan should be implemented on these water infrastructural assets. This study presents a methodology to identify and quantify the level of I/I into the sewer system. Amount of I/I is evaluated by accurate flow measurement in separated sewer systems for specified isolated catchments in Trondheim city (Norway). Advanced information about the characteristics of I/I is gained by CCTV inspection of sewer pipelines with high I/I contribution. Achieving enhanced knowledge about the detection and localization of non-sewer water in foul sewer system during the wet and dry weather conditions will enable the possibility for finding the problem of sewer system and prioritizing them and taking decisions for rehabilitation and renewal planning in the long-term. Furthermore, preventive measures and optimization of sewer systems functionality and efficiency can be executed by maintenance of sewer system. In this way, the exploitation of sewer system can be improved by maintenance and rehabilitation of existing pipelines in a sustainable way by more practical cost-effective and environmental friendly way. This study is conducted on specified catchments with different properties in Trondheim city. Risvollan catchment is one of these catchments with a measuring station to investigate hydrological parameters through the year, which also has a good database. For assessing the infiltration in a separated sewer system, applying the flow rate measurement method can be utilized in obtaining a general view of the network condition from infiltration point of view. This study discusses commonly used and advanced methods of localizing and quantifying I/I in sewer systems. A combination of these methods give sewer operators the possibility to compare different techniques and obtain reliable and accurate I/I data which is vital for long-term rehabilitation plans.

Keywords: flow rate measurement, infiltration and inflow (I/I), non-sewer water, separated sewer systems, sustainable management

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Authors: Abdelkader Hadjoudja, Hajar Bouazza

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The PLD/CPLD devices are widely used for logic synthesis since several decades. Based on sum of product terms (PTs) architecture, the PLD/CPLD offer a high degree of flexibility to support various application requirements. They are suitable for large combinational logic, finite state machines as well as intensive I/O designs. CPLDs offer very predictable timing characteristics and are therefore ideal for critical control applications. This paper describes how the logic synthesis techniques, such as 1) XOR detection, 2) logic doubling, 3) complement of a Boolean function are combined, applied and used to optimize the CPLDs devices architecture that is based on PAL-like macrocells. Our goal is to use these techniques for minimizing the number of macrocells required to implement a circuit and minimize the delay of mapped circuit.

Keywords: CPLD, doubling, optimization, XOR

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Authors: Mustafa Alshawaqfeh, Bilal Wajidy, Echin Serpedin, Jan Suchodolski

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Inflammatory Bowel disease (IBD) is a disease of the colon with characteristic inflammation. Clinically IBD is detected using laboratory tests (blood and stool), radiology tests (imaging using CT, MRI), capsule endoscopy and endoscopy. There are two variants of IBD referred to as Ulcerative Colitis (UC) and Crohn’s disease. This study employs a hybrid feature selection method that combines a correlation-based variable ranking approach with exhaustive search wrapper methods in order to find potential biomarkers for UC. The proposed biomarkers presented accurate discriminatory power thereby identifying themselves to be possible ingredients to UC therapeutics.

Keywords: ulcerative colitis, biomarker detection, feature selection, inflammatory bowel disease (IBD)

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Authors: Morteza Raki, Abolghasem Zabihollah, Omid Askari

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Cantilever beam is a simplified sample of a lot of mechanical components used in a wide range of applications, including many industries such as gas turbine blade. Due to the nature of the operating conditions, beams are subject to variety of damages especially crack propagates. Crack propagation may lead to catastrophic failure during operation. Therefore, online detection of crack presence and its propagation is very important and may reduce possible significant cost of the whole system failure. This paper aims to investigate the effect of cracks presence and crack propagation on one end fixed beam`s vibration. A finite element model will be developed for the blade in which the modal response of the structure with and without crack will be studied. 

Keywords: blade, crack propagation, health monitoring, modal analysis

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Authors: Rui Wang, Luodi Yu, Dan Huang, Hsuan-Chih Chen, Yang Zhang, Suiping Wang

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Enhanced discrimination of pure tones but diminished discrimination of speech pitch (i.e., lexical tone) were found in children with autism who speak a tonal language (Mandarin), suggesting a speech-specific impairment of pitch perception in these children. However, in tonal languages, both lexical tone and vowel are phonemic cues and integrally dependent on each other. Therefore, it is unclear whether the presence of phonemic vowel dimension contributes to the observed lexical tone deficits in Mandarin-speaking children with autism. The current study employed a multi-feature oddball paradigm to examine how vowel and tone dimensions contribute to the neural responses for syllable change detection and involuntary attentional orienting in school-age Mandarin-speaking children with autism. In the oddball sequence, syllable /da1/ served as the standard stimulus. There were three deviant stimulus conditions, representing tone-only change (TO, /da4/), vowel-only change (VO, /du1/), and change of tone and vowel simultaneously (TV, /du4/). EEG data were collected from 25 children with autism and 20 age-matched normal controls during passive listening to the stimulation. For each deviant condition, difference waveform measuring mismatch negativity (MMN) was derived from subtracting the ERP waveform to the standard sound from that to the deviant sound for each participant. Additionally, the linear summation of TO and VO difference waveforms was compared to the TV difference waveform, to examine whether neural sensitivity for TV change detection reflects simple summation or nonlinear integration of the two individual dimensions. The MMN results showed that the autism group had smaller amplitude compared with the control group in the TO and VO conditions, suggesting impaired discriminative sensitivity for both dimensions. In the control group, amplitude of the TV difference waveform approximated the linear summation of the TO and VO waveforms only in the early time window but not in the late window, suggesting a time course from dimensional summation to nonlinear integration. In the autism group, however, the nonlinear TV integration was already present in the early window. These findings suggest that speech perception atypicality in children with autism rests not only in the processing of single phonemic dimensions, but also in the dimensional integration process.

Keywords: autism, event-related potentials , mismatch negativity, speech perception

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Authors: Isa Moazen, Ali Nahvi

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Driver drowsiness is a major cause of vehicle accidents, and facial images are highly valuable to detect drowsiness. In this paper, we perform our research via a thermal camera to record drivers' facial images on a driving simulator. A robust real-time algorithm extracts the features using horizontal and vertical integration projection, contours, contour orientations, and cropping tools. The features are included four target areas on the cheeks and forehead. Qt compiler and OpenCV are used with two cameras with different resolutions. A high-resolution thermal camera is used for fifteen subjects, and a low-resolution one is used for a person. The results are investigated by four temperature plots and evaluated by observer rating of drowsiness.

Keywords: advanced driver assistance systems, thermal imaging, driver drowsiness detection, feature extraction

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Authors: Jing Qing-feng, Liu Danmei

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Based on the optimum detection, signal projection and Maximum A Posteriori (MAP) rule, Proakis has deduced the theoretical BER equation of Gray coded MPSK signals. Proakis analyzed the BER theoretical equations mainly based on the projection of signals, which is difficult to be understood. This article solve the same problem based on the signal space, which explains the vectors relations among the sending signals, received signals and noises. The more explicit and easy-deduced process is illustrated in this article based on the signal space, which can illustrated the relations among the signals and noises clearly. This kind of deduction has a univocal geometry meaning. It can explain the correlation between the production and calculation of BER in vector level.

Keywords: MPSK, MAP, signal space, BER

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Authors: Jheanel E. Estrada, Larry A. Vea

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This study developed model to detect proper/improper sitting posture using the built in web camera which detects the upper body points’ location and distances (chin, manubrium and acromion process). It also established relationships of human body frames and proper sitting posture. The models were developed by training some well-known classifiers such as KNN, SVM, MLP, and Decision Tree using the data collected from 60 students of different body frames. Decision Tree classifier demonstrated the most promising model performance with an accuracy of 95.35% and a kappa of 0.907 for head and shoulder posture. Results also showed that there were relationships between body frame and posture through Body Mass Index.

Keywords: posture, spinal points, gyroscope, image processing, ergonomics

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Authors: Fatma Gülden Şi̇mşek, Osman Meli̇h Can, Mehmet Yumak, Bora Gari̇pcan, Yekta Ülgen

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In this study, the impedance spectroscopy is used as a detection tool in order to characterize surface coating with graphene oxide. Gold electrodes are produced by standard lithography procedures and then coated with graphene oxide using self-assembly method. The impedance of redox solution through bare gold electrodes and graphene oxide coated gold electrodes is measured in the low and high frequency range. The graphene oxide coating reduces the impedance value of the gold electrode and this reduction is distinguishable in the low-frequency range.

Keywords: bioimpedance, electrode characterization, graphene oxide, gold electrodes, impedance spectroscopy

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Authors: Chandimal Jayawardena, Ridmal Mendis, Manoji Tennakoon, Theekshana Wijayathilaka, Randima Marasinghe

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This research paper defines the research area of the implementation of the socially assistive robot (SLIITBOT). It consists of the overall process implemented within the robot’s system and limitations, along with a literature survey. This project considers developing a socially assistive robot called SLIITBOT that will interact using its voice outputs and graphical user interface with people within the university and benefit them with updates and tasks. The robot will be able to detect a person when he/she enters the room, navigate towards the position the human is standing, welcome and greet the particular person with a simple conversation using its voice, introduce the services through its voice, and provide the person with services through an electronic input via an app while guiding the person with voice outputs.

Keywords: application, detection, dialogue, navigation

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Authors: Ferawati, H. Purwanto, Y. F. Kurnia, E. Purwati

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The aim of this study was to evaluate the microbiological quality and safety of meatball obtained from five different manufacturers around Payakumbuh City, West Sumatra, Indonesia. Microbiological analysis of meatball sample resulted in aerobic plate count range from 7 log CFU/gr to 8.623 log CFU/gr, respectively. Total coliform ranges from 1.041 log Most Probable Number (MPN)/gr to 3.380 log MPN/gr, respectively. Chemical analysis of meatball sample consisted of borax and formalin content. The result of qualitative detection of borax and formalin content on all meatball samples were not detected. Thus, it remains essential to include the significance of effective hygiene practices as an important safety measure in consumer education programmes.

Keywords: borax, formalin, meatball, microbiological quality

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Authors: Fahimeh Palizban, Farshad Noravesh, Amir Hossein Saeidian, Mahya Mehrmohamadi

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In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the blood and contribute to a pool of circulating fragments called cell-free DNA. Accordingly, identifying the tissue origin of these DNA fragments from the plasma can result in more accurate and fast disease diagnosis and precise treatment protocols. Open chromatin regions are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. There have been several studies in the area of cancer liquid biopsy that integrate distinct genomic and epigenomic features for early cancer detection along with tissue of origin detection. However, multimodal analysis requires several types of experiments to cover the genomic and epigenomic aspects of a single sample, which will lead to a huge amount of cost and time. To overcome these limitations, the idea of predicting OCRs from WGS is of particular importance. In this regard, we proposed a computational approach to target the prediction of open chromatin regions as an important epigenetic feature from cell-free DNA whole genome sequence data. To fulfill this objective, local sequencing depth will be fed to our proposed algorithm and the prediction of the most probable open chromatin regions from whole genome sequencing data can be carried out. Our method integrates the signal processing method with sequencing depth data and includes count normalization, Discrete Fourie Transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of the clustering (open chromatin region+, open chromatin region-) with previously validated open chromatin regions related to human blood samples of the ATAC-DB database. The percentage of overlap between predicted open chromatin regions and the experimentally validated regions obtained by ATAC-seq in ATAC-DB is greater than 67%, which indicates meaningful prediction. As it is evident, OCRs are mostly located in the transcription start sites (TSS) of the genes. In this regard, we compared the concordance between the predicted OCRs and the human genes TSS regions obtained from refTSS and it showed proper accordance around 52.04% and ~78% with all and the housekeeping genes, respectively. Accurately detecting open chromatin regions from plasma cell-free DNA-seq data is a very challenging computational problem due to the existence of several confounding factors, such as technical and biological variations. Although this approach is in its infancy, there has already been an attempt to apply it, which leads to a tool named OCRDetector with some restrictions like the need for highly depth cfDNA WGS data, prior information about OCRs distribution, and considering multiple features. However, we implemented a graph signal clustering based on a single depth feature in an unsupervised learning manner that resulted in faster performance and decent accuracy. Overall, we tried to investigate the epigenomic pattern of a cell-free DNA sample from a new computational perspective that can be used along with other tools to investigate genetic and epigenetic aspects of a single whole genome sequencing data for efficient liquid biopsy-related analysis.

Keywords: open chromatin regions, cancer, cell-free DNA, epigenomics, graph signal processing, correlation clustering

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Authors: Laura Victoria Vigoya Morales, David Rolando Suarez Mora

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The problem of detecting enterococcus faecalis is receiving considerable attention with the new cases of beachgoers infected with the bacteria, which can be found in fecal matter. The process detection of this kind of bacteria would be taking a long time, which waste time and money as a result of closing recreation place, like beach or pools. Hence, new methods for automating the process of detecting and recognition of this bacteria has become in a challenge. This article describes a novel approach to detect the enterococcus faecalis bacteria in water by using an octave algorithm, which embody a network neural. This document shows result of performance, quality and integrity of the algorithm.

Keywords: Enterococcus faecalis, image treatment, octave and network neuronal

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Authors: Mahboobeh Eghtesad, Reza Mohseni

Abstract:

We develop methods for detecting a target for orthogonal frequency division multiplexing (OFDM) based radars. As a preliminary step we introduce the target and Gaussian noise models in discrete–time form. Then, resorting to match filter (MF) we derive a detector for two different scenarios: a non-fluctuating target and a Rayleigh fluctuating target. It will be shown that a MF is not suitable for Rayleigh fluctuating targets. In this paper we propose a reduced-complexity method based on fast Fourier transfrom (FFT) for such a situation. The proposed method has better detection performance.

Keywords: constant false alarm rate (CFAR), match filter (MF), fast Fourier transform (FFT), OFDM radars, Rayleigh fluctuating target

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