Search results for: sesimic data processing

Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Jianwei Ma, Diriba Gemechu

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In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm.

Keywords: anisotropic total fractional order variation, fractional order bounded variation, seismic random noise attenuation, split Bregman algorithm

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Authors: Andrey V. Timofeev, Dmitry Egorov

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This paper describes an effective solution to the task of a remote monitoring of super-extended objects (oil and gas pipeline, railways, national frontier). The suggested solution is based on the principle of simultaneously monitoring of seismoacoustic and optical/infrared physical fields. The principle of simultaneous monitoring of those fields is not new but in contrast to the known solutions the suggested approach allows to control super-extended objects with very limited operational costs. So-called C-OTDR (Coherent Optical Time Domain Reflectometer) systems are used to monitor the seismoacoustic field. Far-CCTV systems are used to monitor the optical/infrared field. A simultaneous data processing provided by both systems allows effectively detecting and classifying target activities, which appear in the monitored objects vicinity. The results of practical usage had shown high effectiveness of the suggested approach.

Keywords: C-OTDR monitoring system, bimodal processing, LPboost, SVM

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Authors: W. Ait Ahmed, M. Aggour, M. Naciri

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Extracting energy from biomass is an important alternative to produce different types of energy (heat, electricity, or both) assuring low pollution and better efficiency. It is a new yet reliable approach to reduce green gas emission by extracting methane from industry effluents and use it to power machinery. We focused in our project on using paper and mill effluents, treated in a UASB reactor. The methane produced is used in the factory’s power supply. The aim of this work is to develop an electronic system using Arduino platform connected to a gas sensor, to measure and display the curve of daily methane production on processing. The sensor will send the gas values in ppm to the Arduino board so that the later sends the RS232 hardware protocol. The code developed with processing will transform the values into a curve and display it on the computer screen.

Keywords: biogas, Arduino, processing, code, methane, gas sensor, program

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: Zohra Mekranfar, Ahmed Saidi, Abdellah Mebrek

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This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system.

Keywords: data warehouse, GIS, MCDM, SOLAP

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Authors: Hee-Choul Kwon, Heeyong Kwon

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Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one.

Keywords: high-speed rotation operation, image rotation, transform matrix, image processing, pattern recognition

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Authors: Shrey Sharma, Gunjan Kumar Verma

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High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.

Keywords: inversion, limitations, optimization, resistivity

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Authors: Paula Lopez-Arce, Hector Altamirano, Dimitrios Rovas, James Berry, Bryan Hindle, Steven Hodgson

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Efficient mould remediation and accurate moisture diagnostics leading to condensation and mould growth in dwellings are largely untapped. Number of factors are contributing to the rising trend of excessive moisture in homes mainly linked with modern living, increased levels of occupation and rising fuel costs, as well as making homes more energy efficient. Environmental monitoring by means of data collection though loggers sensors and survey forms has been performed in a range of buildings from different UK regions. Air and surface temperature and relative humidity values of residential areas affected by condensation and/or mould issues were recorded. Additional measurements were taken through different trials changing type, location, and position of loggers. In some instances, IR thermal images and ventilation rates have also been acquired. Results have been interpreted together with environmental key parameters by processing and connecting data from loggers and survey questionnaires, both in buildings with and without moisture issues. Monitoring exercises carried out during Winter and Spring time show the importance of developing and following accurate protocols for guidance to obtain consistent, repeatable and comparable results and to improve the performance of environmental monitoring. A model and a protocol are being developed to build a diagnostic tool with the goal of performing a simple but precise residential atmospheric moisture diagnostics to distinguish the cause entailing condensation and mould generation, i.e., ventilation, insulation or heating systems issue. This research shows the relevance of monitoring and processing environmental data to assign moisture risk levels and determine the origin of condensation or mould when dealing with a building atmospheric moisture excess.

Keywords: environmental monitoring, atmospheric moisture, protocols, mould

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Authors: Teodora Vukovic, Christoph Hottiger, Noah Bubenhofer

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The development of VIAN-DH aims at bridging two linguistic approaches: conversation analysis/interactional linguistics (IL), so far a dominantly qualitative field, and computational/corpus linguistics and its quantitative and automated methods. Contemporary IL investigates the systematic organization of conversations and interactions composed of speech, gaze, gestures, and body positioning, among others. These highly integrated multimodal behaviour is analysed based on video data aimed at uncovering so called “multimodal gestalts”, patterns of linguistic and embodied conduct that reoccur in specific sequential positions employed for specific purposes. Multimodal analyses (and other disciplines using videos) are so far dependent on time and resource intensive processes of manual transcription of each component from video materials. Automating these tasks requires advanced programming skills, which is often not in the scope of IL. Moreover, the use of different tools makes the integration and analysis of different formats challenging. Consequently, IL research often deals with relatively small samples of annotated data which are suitable for qualitative analysis but not enough for making generalized empirical claims derived quantitatively. VIAN-DH aims to create a workspace where many annotation layers required for the multimodal analysis of videos can be created, processed, and correlated in one platform. VIAN-DH will provide a graphical interface that operates state-of-the-art tools for automating parts of the data processing. The integration of tools that already exist in computational linguistics and computer vision, facilitates data processing for researchers lacking programming skills, speeds up the overall research process, and enables the processing of large amounts of data. The main features to be introduced are automatic speech recognition for the transcription of language, automatic image recognition for extraction of gestures and other visual cues, as well as grammatical annotation for adding morphological and syntactic information to the verbal content. In the ongoing instance of VIAN-DH, we focus on gesture extraction (pointing gestures, in particular), making use of existing models created for sign language and adapting them for this specific purpose. In order to view and search the data, VIAN-DH will provide a unified format and enable the import of the main existing formats of annotated video data and the export to other formats used in the field, while integrating different data source formats in a way that they can be combined in research. VIAN-DH will adapt querying methods from corpus linguistics to enable parallel search of many annotation levels, combining token-level and chronological search for various types of data. VIAN-DH strives to bring crucial and potentially revolutionary innovation to the field of IL, (that can also extend to other fields using video materials). It will allow the processing of large amounts of data automatically and, the implementation of quantitative analyses, combining it with the qualitative approach. It will facilitate the investigation of correlations between linguistic patterns (lexical or grammatical) with conversational aspects (turn-taking or gestures). Users will be able to automatically transcribe and annotate visual, spoken and grammatical information from videos, and to correlate those different levels and perform queries and analyses.

Keywords: multimodal analysis, corpus linguistics, computational linguistics, image recognition, speech recognition

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Authors: Andrey V. Timofeev

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The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data off the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as 'signaling parameters' (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of C-OTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as a rule. This report contains describing the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.

Keywords: C-OTDR-system, co-processing of signaling parameters, high-speed objects localization, multichannel monitoring systems

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Authors: Schnell Zsuzsanna

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Background: The interpretation of written texts, language processing in the visual domain, in other words, atypical reading abilities, also known as dyslexia, is an ever-growing phenomenon in today’s societies and educational communities. The much-researched problem affects cognitive abilities and, coupled with normal intelligence normally manifests difficulties in the differentiation of sounds and orthography and in the holistic processing of written words. The factors of susceptibility are varied: social, cognitive psychological, and linguistic factors interact with each other. Methods: The research will explain the psycholinguistics of dyslexia on the basis of several empirical experiments and demonstrate how domain-general abilities of inhibition, retrieval from the mental lexicon, priming, phonological processing, and visual modality transfer affect successful language processing and interpretation. Interpretation of visual stimuli is hindered, and the problem seems to be embedded in a sociocultural, psycholinguistic, and cognitive background. This makes the picture even more complex, suggesting that the understanding and resolving of the issues of dyslexia has to be interdisciplinary, aided by several disciplines in the field of humanities and social sciences, and should be researched from an empirical approach, where the practical, educational corollaries can be analyzed on an applied basis. Aim and applicability: The lecture sheds light on the applied, cognitive aspects of interpretation, social cognitive traits of language processing, the mental underpinnings of cognitive interpretation strategies in different languages (namely, Hungarian and English), offering solutions with a few applied techniques for success in foreign language learning that can be useful advice for the developers of testing methodologies and measures across ESL teaching and testing platforms.

Keywords: dyslexia, social cognition, transparency, modalities

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Authors: Arman S. Kussainov, Altynbek K. Beisekov

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This work focuses on building an efficient universal procedure to construct a single density of states from the multiple pieces of data provided by the parallel implementation of the Wang Landau Monte Carlo based algorithm. The Ising and Pott models were used as the examples of the two-dimensional spin lattices to construct their densities of states. Sampled energy space was distributed between the individual walkers with certain overlaps. This was made to include the latest development of the algorithm as the density of states replica exchange technique. Several factors of immediate importance for the seamless stitching process have being considered. These include but not limited to the speed and universality of the initial parallel algorithm implementation as well as the data post-processing to produce the expected smooth density of states.

Keywords: density of states, Monte Carlo, parallel algorithm, Wang Landau algorithm

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Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.

Keywords: camera calibration, ice detection, ice load measurements, image processing

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Ryan Messina, Mehedi Hasan

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This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: T. Gangadhararao, K. Krishna Kishore

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Motion Estimation (ME) plays a critical role in a video coder, testing such a module is of priority concern. While focusing on the testing of ME in a video coding system, this work presents an error detection and data recovery (EDDR) design, based on the residue-and-quotient (RQ) code, to embed into ME for video coding testing applications. An error in processing Elements (PEs), i.e. key components of a ME, can be detected and recovered effectively by using the proposed EDDR design. The proposed EDDR design for ME testing can detect errors and recover data with an acceptable area overhead and timing penalty.

Keywords: area overhead, data recovery, error detection, motion estimation, reliability, residue-and-quotient (RQ) code

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Authors: Chin-Chih Chiou, Tai-Yuan Su, Ti-Yu Chen, Wen-Yu Chiu, Chungyu Chen

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The purpose of this study was to investigate the effect of reaction time (RT) or balance performance as the number of stimulus-response choices increases, the amount of information processing of 0-bit and 1-bit conditions based on Hick’s law, using the dual-task design. Eighteen children (age: 9.38 ± 0.27 years old) were recruited as the participants for this study, and asked to assess RT and balance performance separately and simultaneously as following five conditions: simple RT (0-bit decision), choice RT (1-bit decision), single balance control, balance control with simple RT, and balance control with choice RT. Biodex 950-300 balance system and You-Shang response timer were used to record and analyze the postural stability and information processing speed (RT) respectively for the participants. Repeated measures one-way ANOVA with HSD post-hoc test and 2 (balance) × 2 (amount of information processing) repeated measures two-way ANOVA were used to test the parameters of balance performance and RT (α = .05). The results showed the overall stability index in the 1-bit decision was lower than in 0-bit decision, and the mean deflection in the 1-bit decision was lower than in single balance performance. Simple RTs were faster than choice RTs both in single task condition and dual task condition. It indicated that the chronometric approach of RT could use to infer the attention requirement of the secondary task. However, this study did not find that the balance performance is interfered for children by the increasing of the amount of information processing.

Keywords: capacity theory, reaction time, Hick’s law, balance

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Authors: S. Raghibdoust, E. Kamari

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Research show that reduced functional hearing has a detrimental influence on the ability of an individual to establish proper phonological representations of words, since the phonological representations are claimed to mediate the conceptual processing of written words. Word processing efficiency is expected to decrease with a decrease in functional hearing. In other words, it is predicted that hearing individuals would be more capable of word processing than individuals with hearing loss, as their functional hearing works normally. Studies also demonstrate that the quality of the functional hearing affects reading comprehension via its effect on their word processing skills. In other words, better hearing facilitates the development of phonological knowledge, and can promote enhanced strategies for the recognition of written words, which in turn positively affect higher-order processes underlying reading comprehension. The aims of this study were to investigate and compare the effect of deafness on the participants’ abilities to process written words at the lexical and sentence levels through using two online and one offline reading comprehension tests. The performance of a group of 8 deaf male students (ages 8-12) was compared with that of a control group of normal hearing male students. All the participants had normal IQ and visual status, and came from an average socioeconomic background. None were diagnosed with a particular learning or motor disability. The language spoken in the homes of all participants was Persian. Two tests of word processing were developed and presented to the participants using OpenSesame software, in order to measure the speed and accuracy of their performance at the two perceptual and conceptual levels. In the third offline test of reading comprehension which comprised of semantically plausible and semantically implausible subject relative clauses, the participants had to select the correct answer out of two choices. The data derived from the statistical analysis using SPSS software indicated that hearing and deaf participants had a similar word processing performance both in terms of speed and accuracy of their responses. The results also showed that there was no significant difference between the performance of the deaf and hearing participants in comprehending semantically plausible sentences (p > 0/05). However, a significant difference between the performances of the two groups was observed with respect to their comprehension of semantically implausible sentences (p < 0/05). In sum, the findings revealed that the seriously impoverished sentence reading ability characterizing the profound deaf subjects of the present research, exhibited their reliance on reading strategies that are based on insufficient or deviant structural knowledge, in particular in processing semantically implausible sentences, rather than a failure to efficiently process written words at the lexical level. This conclusion, of course, does not mean to say that deaf individuals may never experience deficits at the word processing level, deficits that impede their understanding of written texts. However, as stated in previous researches, it sounds reasonable to assume that the more deaf individuals get familiar with written words, the better they can recognize them, despite having a profound phonological weakness.

Keywords: deafness, reading comprehension, reading strategy, word processing, subject and object relative sentences

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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Authors: Ruchi Yadav, Shivani Pandey, Prachi Srivastava

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Microarrays provide a rich source of data on the molecular working of cells. Each microarray reports on the abundance of tens of thousands of mRNAs. Virtually every human disease is being studied using microarrays with the hope of finding the molecular mechanisms of disease. Bioinformatics analysis plays an important part of processing the information embedded in large-scale expression profiling studies and for laying the foundation for biological interpretation. A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. One of the most popular platforms for microarray analysis is Bioconductor, an open source and open development software project based on the R programming language. This paper describes specific procedures for conducting quality assessment, visualization and preprocessing of Affymetrix Gene Chip and also details the different bioconductor packages used to analyze affymetrix microarray data and describe the analysis and outcome of each plots.

Keywords: microarray analysis, R language, affymetrix visualization, bioconductor

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Authors: George Hanna Abdelmelek Henien

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Quality and safety issues are common in Ethiopia's food processing industry, which can negatively impact consumers' health and livelihoods. The country is known for its various agricultural products that are important to the economy. However, food quality and safety policies and management practices in the food processing industry have led to many health problems, foodborne illnesses and economic losses. This article aims to show the causes and consequences of food safety and quality problems in the food processing industry in Ethiopia and discuss possible solutions to solve them. One of the main reasons for food quality and safety in Ethiopia's food processing industry is the lack of adequate regulation and enforcement mechanisms. Inadequate food safety and quality policies have led to inefficiencies in food production. Additionally, the failure to monitor and enforce existing regulations has created a good opportunity for unscrupulous companies to engage in harmful practices that endanger the lives of citizens. The impact on food quality and safety is significant due to loss of life, high medical costs, and loss of consumer confidence in the food processing industry. Foodborne diseases such as diarrhoea, typhoid and cholera are common in Ethiopia, and food quality and safety play an important role in . Additionally, food recalls due to contamination or contamination often cause significant economic losses in the food processing industry. To solve these problems, the Ethiopian government began taking measures to improve food quality and safety in the food processing industry. One of the most prominent initiatives is the Ethiopian Food and Drug Administration (EFDA), which was established in 2010 to monitor and control the quality and safety of food and beverage products in the country. EFDA has implemented many measures to improve food safety, such as carrying out routine inspections, monitoring the import of food products and implementing labeling requirements. Another solution that can improve food quality and safety in the food processing industry in Ethiopia is the implementation of food safety management system (FSMS). FSMS is a set of procedures and policies designed to identify, assess and control food safety risks during food processing. Implementing a FSMS can help companies in the food processing industry identify and address potential risks before they harm consumers. Additionally, implementing an FSMS can help companies comply with current safety and security regulations. Consequently, improving food safety policy and management system in Ethiopia's food processing industry is important to protect people's health and improve the country's economy. . Addressing the root causes of food quality and safety and implementing practical solutions that can help improve the overall food safety and quality in the country, such as establishing regulatory bodies and implementing food management systems.

Keywords: food quality, food safety, policy, management system, food processing industry food traceability, industry 4.0, internet of things, block chain, best worst method, marcos

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Authors: Rachna Jain, Sushila Madan, Bindu Garg

Abstract:

Cloud computing is the key powerhouse in numerous organizations due to shifting of their data to the cloud environment. In recent years it has been observed that cloud-based-services are being used on large scale for content storage, distribution and processing. Various issues have been observed in cloud computing environment that need to be addressed. Security and privacy are found topmost concern area. In this paper, a novel security model is proposed to secure data by utilizing CDN services like image to icon conversion. CDN Service is a content delivery service which converts an image to icon, word to pdf & Latex to pdf etc. Presented model is used to convert an image into icon by keeping image secret. Here security of image is imparted so that image should be encrypted and decrypted by data owners only. It is also discussed in the paper that how server performs multiplication and selection on encrypted data without decryption. The data can be image file, word file, audio or video file. Moreover, the proposed model is capable enough to multiply images, encrypt them and send to a server application for conversion. Eventually, the prime objective is to encrypt an image and convert the encrypted image to image Icon by utilizing homomorphic encryption.

Keywords: cloud computing, user data security, homomorphic encryption, image multiplication, CDN service

Procedia PDF Downloads 322

Authors: Sandeep Sharma, Sarabjit Singh

Abstract:

As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data.

Keywords: big data, unstructured data, volume, variety, velocity

Procedia PDF Downloads 527

Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

Abstract:

Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

Procedia PDF Downloads 107

Authors: Kavian Khosravinia, Mohd Khair Hassan, Ribhan Zafira Abdul Rahman, Syed Abdul Rahman Al-Haddad

Abstract:

The CAN (controller area network) bus is introduced as a multi-master, message broadcast system. The messages sent on the CAN are used to communicate state information, referred as a signal between different ECUs, which provides data consistency in every node of the system. OBD-II Dongles that are based on request and response method is the wide-spread solution for extracting sensor data from cars among researchers. Unfortunately, most of the past researches do not consider resolution and quantity of their input data extracted through OBD-II technology. The maximum feasible scan rate is only 9 queries per second which provide 8 data points per second with using ELM327 as well-known OBD-II dongle. This study aims to develop and design a programmable, and latency-sensitive vehicle data acquisition system that improves the modularity and flexibility to extract exact, trustworthy, and fresh car sensor data with higher frequency rates. Furthermore, the researcher must break apart, thoroughly inspect, and observe the internal network of the vehicle, which may cause severe damages to the expensive ECUs of the vehicle due to intrinsic vulnerabilities of the CAN bus during initial research. Desired sensors data were collected from various vehicles utilizing Raspberry Pi3 as computing and processing unit with using OBD (request-response) and direct CAN method at the same time. Two types of data were collected for this study. The first, CAN bus frame data that illustrates data collected for each line of hex data sent from an ECU and the second type is the OBD data that represents some limited data that is requested from ECU under standard condition. The proposed system is reconfigurable, human-readable and multi-task telematics device that can be fitted into any vehicle with minimum effort and minimum time lag in the data extraction process. The standard operational procedure experimental vehicle network test bench is developed and can be used for future vehicle network testing experiment.

Keywords: CAN bus, OBD-II, vehicle data acquisition, connected cars, telemetry, Raspberry Pi3

Procedia PDF Downloads 182

Authors: Rahele Mesbah, Nic Van Der Wee, Manja Koenders, Erik Giltay, Albert Van Hemert, Max De Leeuw

Abstract:

Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression.

Keywords: cognitive functioning, fMRI analysis, bipolar disorder, fronto-limbic network

Procedia PDF Downloads 441