Search results for: radiation processing

Authors: Hema Bhardwaj, D. Srinivasa Rao

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Technology for analysing, processing, and extracting meaningful data from enormous and complicated datasets can be termed as "big data." The technique of big data mining and big data analysis is extremely helpful for business movements such as making decisions, building organisational plans, researching the market efficiently, improving sales, etc., because typical management tools cannot handle such complicated datasets. Special computational and statistical issues, such as measurement errors, noise accumulation, spurious correlation, and storage and scalability limitations, are brought on by big data. These unique problems call for new computational and statistical paradigms. This research paper offers an overview of the literature on big data mining, its process, along with problems and difficulties, with a focus on the unique characteristics of big data. Organizations have several difficulties when undertaking data mining, which has an impact on their decision-making. Every day, terabytes of data are produced, yet only around 1% of that data is really analyzed. The idea of the mining and analysis of data and knowledge discovery techniques that have recently been created with practical application systems is presented in this study. This article's conclusion also includes a list of issues and difficulties for further research in the area. The report discusses the management's main big data and data mining challenges.

Keywords: big data, data mining, data analysis, knowledge discovery techniques, data mining challenges

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Authors: Carlos Vera, Tetyana Baydyk, Ernst Kussul, Graciela Velasco, Miguel Aparicio

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This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed.

Keywords: contour orientation histogram, meteors, night sky, RSC neural classifier, stars

Procedia PDF Downloads 126

Authors: Halil Ibrahim Demir, Caner Erden, Abdullah Hulusi Kokcam, Mumtaz Ipek

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Process planning, scheduling, and due date assignment are three important manufacturing functions which are studied independently in literature. There are hundreds of works on IPPS and SWDDA problems but a few works on IPPSDDA problem. Integrating these three functions is very crucial due to the high relationship between them. Since the scheduling problem is in the NP-Hard problem class without any integration, an integrated problem is even harder to solve. This study focuses on the integration of these functions. Sum of weighted tardiness, earliness, and due date related costs are used as a penalty function. Random search and hybrid metaheuristics are used to solve the integrated problem. Marginal improvement in random search is very high in the early iterations and reduces enormously in later iterations. At that point directed search contribute to marginal improvement more than random search. In this study, random and genetic search methods are combined to find better solutions. Results show that overall performance becomes better as the integration level increases.

Keywords: process planning, genetic algorithm, hybrid search, random search, weighted due-date assignment, weighted scheduling

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Authors: Dessie Tibebe, Muluken Asmare, Marye Mulugeta, Yezbie Kassa, Zerubabel Moges, Dereje Yenealem, Tarekegn Fentie, Agmas Amare

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The performance of electrocoagulation (EC) using Aluminium electrodes for the treatment of effluent-containing chromium metal using a fixed bed electrochemical batch reactor was studied. In the present work, the efficiency evaluation of EC in removing physicochemical and heavy metals from real industrial tannery wastewater in the Amhara region, collected from Bahirdar, Debre Brihan, and Haik, was investigated. The treated and untreated samples were determined by AAS and ICP OES spectrophotometers. The results indicated that selected heavy metals were removed in all experiments with high removal percentages. The optimal results were obtained regarding both cost and electrocoagulation efficiency with initial pH = 3, initial concentration = 40 mg/L, electrolysis time = 30 min, current density = 40 mA/cm2, and temperature = 25oC favored metal removal. The maximum removal percentages of selected metals obtained were 84.42% for Haik, 92.64% for Bahir Dar and 94.90% for Debre Brihan. The sacrificial electrode and sludge were characterized by FT-IR, SEM and XRD. After treatment, some metals like chromium will be used again as a tanning agent in leather processing to promote a circular economy.

Keywords: electrochemical, treatment, aluminum, tannery effluent

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Authors: Mehrnoosh Ghadimi, Golnoush Ghadimi

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Precise monitoring of glacier velocity is critical in determining glacier-related hazards. Zardkooh Mountain was studied in terms of glacial activity rate in Zagros Mountainous region in Iran. In this study, we assessed the ability of optical and radar imagery to derive glacier-surface velocities in mountainous terrain. We processed Landsat 8 for optical data and Sentinel-1a for radar data. We used methods that are commonly used to measure glacier surface movements, such as cross correlation of optical and radar satellite images, SAR tracking techniques, and multiple aperture InSAR (MAI). We also assessed time series glacier surface displacement using our modified method, Enhanced Small Baseline Subset (ESBAS). The ESBAS has been implemented in StaMPS software, with several aspects of the processing chain modified, including filtering prior to phase unwrapping, topographic correction within three-dimensional phase unwrapping, reducing atmospheric noise, and removing the ramp caused by ionosphere turbulence and/or orbit errors. Our findings indicate an average surface velocity rate of 32 mm/yr in the Zardkooh mountainous areas.

Keywords: active rock glaciers, landsat 8, sentinel-1a, zagros mountainous region

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Authors: Ouafa Benaida, Abdelhamid Loukil, Adda Ali Pacha

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In recent years, the growing interest of scientists in the field of image processing and analysis of underwater images and videos has been strengthened following the emergence of new underwater exploration techniques, such as the emergence of autonomous underwater vehicles and the use of underwater image sensors facilitating the exploration of underwater mineral resources as well as the search for new species of aquatic life by biologists. Indeed, underwater images and videos have several defects and must be preprocessed before their analysis. Underwater landscapes are usually darkened due to the interaction of light with the marine environment: light is absorbed as it travels through deep waters depending on its wavelength. Additionally, light does not follow a linear direction but is scattered due to its interaction with microparticles in water, resulting in low contrast, low brightness, color distortion, and restricted visibility. The improvement of the underwater image is, therefore, more than necessary in order to facilitate its analysis. The research presented in this paper aims to implement and evaluate a set of classical techniques used in the field of improving the quality of underwater images in several color representation spaces. These methods have the particularity of being simple to implement and do not require prior knowledge of the physical model at the origin of the degradation.

Keywords: underwater image enhancement, histogram normalization, histogram equalization, contrast limited adaptive histogram equalization, single-scale retinex

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Authors: Carolina Gouveia, José Vieira, Pedro Pinho

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The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully.

Keywords: bio-signals, DC component, Doppler effect, ellipse fitting, radar, SDR

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi

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In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.

Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing

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Authors: F. Ghani, T. S. O’Donovan

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Today, the global solar thermal market is dominated by two collector types; the flat plate and evacuated tube collector. With regards to the number of installations worldwide, the evacuated tube collector is the dominant variant primarily due to the Chinese market but the flat plate collector dominates both the Australian and European markets. The market share of the evacuated tube collector is, however, growing in Australia due to a common belief that this collector type is ‘more efficient’ and, therefore, the better choice for hot water applications. In this study, we investigate this issue further to assess the validity of this statement. This was achieved by methodically comparing the performance and economics of several solar thermal systems comprising of; a low-performance flat plate collector, a high-performance flat collector, and an evacuated tube collector coupled with a storage tank and pump. All systems were simulated using the commercial software package Polysun for four climate zones in Australia to take into account different weather profiles in the study and subjected to a thermal load equivalent to a household comprising of four people. Our study revealed that the energy savings and payback periods varied significantly for systems operating under specific environmental conditions. Solar fractions ranged between 58 and 100 per cent, while payback periods range between 3.8 and 10.1 years. Although the evacuated tube collector was found to operate with a marginally higher thermal efficiency over the selective surface flat plate collector due to reduced ambient heat loss, the high-performance flat plate collector outperformed the evacuated tube collector on thermal yield. This result was obtained as the flat plate collector possesses a significantly higher absorber to gross collector area ratio over the evacuated tube collector. Furthermore, it was found for Australian regions operating with a high average solar radiation intensity and ambient temperature, the lower performance collector is the preferred choice due to favorable economics and reduced stagnation temperature. Our study has provided additional insight into the thermal performance and economics of the two prevalent solar thermal collectors currently available. A computational investigation has been carried out specifically for the Australian climate due to its geographic size and significant variation in weather. For domestic hot water applications were fluid temperatures between 50 and 60 degrees Celsius are sought, the flat plate collector is both technically and economically favorable over the evacuated tube collector. This research will be useful to system design engineers, solar thermal manufacturers, and those involved in policy to encourage the implementation of solar thermal systems into the hot water market.

Keywords: solar thermal, energy analysis, flat plate, evacuated tube, collector performance

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Authors: Gabriela Kalcikova, Gregor Marolt, Anita Jemec Kokalj, Andreja Zgajnar Gotvajn

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Since the 90’s, nanotechnology is one of the fastest growing fields of science. Nanomaterials are increasingly becoming part of many products and technologies. Metal oxide nanoparticles are among the most used nanomaterials. Zinc oxide nanoparticles (nZnO) is widely used due to its versatile properties; it has been used in products including plastics, paints, food, batteries, solar cells and cosmetic products. It is also a very effective photocatalyst used for water treatment. Such expanding application of nZnO increases their possible occurrence in the environment. In the aquatic ecosystem nZnO interact with natural environmental factors such as UV radiation, and thus it is essential to evaluate possible interaction between them. In this context, the aim of our study was to evaluate combined ecotoxicity of nZnO and Zn²⁺ on duckweed Lemna minor in presence or absence UV. Inhibition of vegetative growth of duckweed Lemna minor was monitored over a period of 7 days in multi-well plates. After the experiment, specific growth rate was determined. ZnO nanoparticles used were of primary size 13.6 ± 1.7 nm. The test was conducted with nominal nZnO and Zn²⁺ (in form of ZnCl₂) concentrations of 1, 10, 100 mg/L. Experiment was repeated with presence of natural intensity of UV (8h UV, 10 W/m² UVA, 0.5 W/m² UVB). Concentration of Zn during the test was determined by ICP-MS. In the regular experiment (absence of UV) the specific growth rate was slightly increased by low concentrations of nZnO and Zn²⁺ in comparison to control. However, 10 and 100 mg/L of Zn²⁺ resulted in 45% and 68% inhibition of the specific growth rate, respectively. In case of nZnO both concentrations (10 and 100 mg/L) resulted in similar ~ 30% inhibition and the response was not dose-dependent. The lack of the dose-response relationship is often observed in case of nanoparticles. The possible explanation is that the physical impact prevails instead of chemical ones. In the presence of UV the toxicity of Zn²⁺ was increased and 100 mg/L of Zn²⁺ caused total inhibition of the specific growth rate (100%). On the other hand, 100 mg/L of nZnO resulted in low inhibition (19%) in comparison to the experiment without UV (30%). It is thus expected, that tested nZnO is low photoactive, but could have a good UV absorption and/or reflective properties and thus protect duckweed against UV impacts. Measured concentration of Zn in the test suspension decreased only about 4% after 168h in the case of ZnCl₂. On the other hand concentration of Zn in nZnO test decreased by 80%. It is expected that nZnO were partially dissolved in the medium and at the same time agglomeration and sedimentation of particles took place and thus the concentration of Zn at the water level decreased. Results of our study indicated, that nZnO combined with UV of natural intensity does not increase toxicity of nZnO, but slightly protect the plant against UV negative effects. When Zn²⁺ and ZnO results are compared it seems that dissolved Zn plays a central role in the nZnO toxicity.

Keywords: duckweed, environmental factors, nanoparticles, toxicity

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Authors: Stanley Fore

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The case study company is one of the leading newsprint companies in South Africa. The company has achieved this status through operational expansion, diversification and investing in cutting-edge technology. They have a reputation for the highest quality and personalised service that transcends borders and industries. The company offers a wide variety of small and large scales printing services. The company is faced with the challenge of significant waste production during normal operations. The company generates 1200 kg of plastic waste and 60 – 70 tonnes of paper waste per month. The company operates a waste management process currently, whereby waste paper is sold, at low cost, to recycling firms for further processing. Having considered the quantity of waste being generated, the company has embarked on a venture to find a more profitable solution to its current waste production. As waste management and recycling is not the company’s core business, the aim of the venture is to implement a secondary profitable waste process business. The venture will be expedited as a strategic project. This research aims to estimate the financial feasibility of a selected solution as well as the impact of non-financial considerations thereof. The financial feasibility is analysed using metrics such as Payback period; internal rate of return and net present value.

Keywords: waste, printing industry, up-cycling, management

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Authors: Amir Amiri, Naghmeh Morakabati

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During the recent years the six-fold growth of cancer in Iran has led the production of healthy products to become a challenge in the food industry. Due to the young population in the country, the consumption of fast foods is growing. The chemical cancer-causing preservatives are used to produce these products more than the standard; so using an appropriate alternative seems to be important. On the one hand, the plant essential oils show the high antimicrobial potential against pathogenic and spoilage microorganisms and on the other hand they are highly volatile and decomposed under the processing conditions. The study aims to produce the loaded chitosan nanoparticles with different concentrations of savory essential oil to improve the anti-microbial property and increase the resistance of essential oil to oxygen and heat. The encapsulation efficiency was obtained in the range of 32.07% to 39.93% and the particle size distribution of the samples was observed in the range of 159 to 210 nm. The range of Zeta potential was obtained between -11.9 to -23.1 mV. The essential oil loaded in chitosan showed stronger antifungal activity against Rhizopus stolonifer. The results showed that the antioxidant property is directly related to the concentration of loaded essential oil so that the antioxidant property increases by increasing the concentration of essential oil. In general, it seems that the savory essential oil loaded in chitosan particles can be used as a food processor.

Keywords: chitosan, encapsulation, essential oil, nanogel

Procedia PDF Downloads 256

Authors: O. E. Okafor, L. U. S. Ezeanyika, C. G. Nkwonta, C. J. Okonkwo

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The effect of processing on plasma lipid profile and atherogenic indices of rats fed Artocarpus heterophyllus seed diets at different concentrations were investigated. Fifty five rats were used for this study, they were divided into eleven groups of five rats each (one control group and ten test groups), the test groups were fed raw, boiled, roasted, fermented, and soaked diets at 10 % and 40% concentrations. The study lasted for thirty five days. The diets led to significant decrease (p < 0.05) in plasma cholesterol and triacylglycerol of rats fed 10% and 40% concentrations of the diets, and a significant increase (p < 0.05) in high density lipoprotein (HDL) levels at 40% concentrations of the test diets. The diets also produced decrease in low density lipoprotein (LDL), very low density lipoprotein (VLDL), cardiac risk ratio (CRR), atherogenic index of plasma (AIP) and atherogenic coefficient (AC) at 40% concentrations except the soaked group that showed slight elevation of LDL, CRR, AC and AIP at 40% concentration. Artocarpus heterophyllus seeds could be beneficial to health because of its ability to increase plasma HDL and reduce plasma LDL, VLDL, cholesterol, triglycerides and atherogenic indices at higher diet concentration.

Keywords: artocarpus heterophyllus, atherogenic indices, concentrations, lipid profile

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Authors: Sangeeta Palekar, Mahima Rana, Jayu Kalambe

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In this paper, a microfluidic-based platform for the quantification of contaminants in the water is proposed. The proposed system uses microfluidic channels with an embedded environment for contaminants detection in water. Microfluidics-based platforms present an evident stage of innovation for fluid analysis, with different applications advancing minimal efforts and simplicity of fabrication. Polydimethylsiloxane (PDMS)-based microfluidics channel is fabricated using a soft lithography technique. Vertical and horizontal connections for fluid dispensing with the microfluidic channel are explored. The principle of colorimetry, which incorporates the use of Griess reagent for the detection of nitrite, has been adopted. Nitrite has high water solubility and water retention, due to which it has a greater potential to stay in groundwater, endangering aquatic life along with human health, hence taken as a case study in this work. The developed platform also compares the detection methodology, containing photodetectors for measuring absorbance and image sensors for measuring color change for quantification of contaminants like nitrite in water. The utilization of image processing techniques offers the advantage of operational flexibility, as the same system can be used to identify other contaminants present in water by introducing minor software changes.

Keywords: colorimetric, fluid contaminants, nitrite detection, microfluidics

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Authors: Anita Vidacs, Robert Rajko, Csaba Vagvolgyi, Judit Krisch

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With the optimization of a new disinfectant the number of tests could be decreased and the cost of processing too. Good sanitizers are eco-friendly and allow no resistance evolvement of bacteria. The essential oils (EOs) are natural antimicrobials, and most of them have the Generally Recognized As Safe (GRAS) status. In our study, the effect of the EOs cinnamon, marjoram, and thyme was investigated against mixed species bacterial biofilms of Escherichia coli, Listeria monocytogenes, Pseudomonas putida, and Staphylococcus aureus. The optimal concentration of EOs, disinfection time and level of pH were evaluated with the aid of Response Surface Box-Behnken Design (RSD) on 1 day and 7 days old biofilms on metal, plastic, and wood surfaces. The variable factors were in the range of 1-3 times of minimum bactericide concentration (MBC); 10-110 minutes acting time and 4.5- 7.5 pH. The optimized EO disinfectant was compared to industrial used chemicals (HC-DPE, Hypo). The natural based disinfectants were applicable; the acting time was below 30 minutes. EOs were able to eliminate the biofilm from the used surfaces except from wood. The disinfection effect of the EO based natural solutions was in most cases equivalent or better compared to chemical sanitizers used in food industry.

Keywords: biofilm, Box-Behnken design, disinfectant, essential oil

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Authors: Shweta Lall

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The intent of this paper is to demonstrate the role of computerized auxiliary science in advancing the desired and necessary alliance of historians, surveyors, topographers, and analysts of architectural conservation and management. The digital era practice of recording architectural and cultural heritage in view of its preservation, dissemination, and planning developments are discussed in this paper. Geomatics include practices like remote sensing, photogrammetry, surveying, Geographic Information System (GIS), laser scanning technology, etc. These all resources help in architectural and conservation applications which will be identified through various case studies analysed in this paper. The standardised outcomes and the methodologies using relevant case studies are listed and described. The main component of geomatics methodology adapted in conservation is data acquisition, processing, and presentation. Geomatics is used in a wide range of activities involved in architectural and cultural heritage – damage and risk assessment analysis, documentation, 3-D model construction, virtual reconstruction, spatial and structural decision – making analysis and monitoring. This paper will project the summary answers of the capabilities and limitations of the geomatics field in architectural and cultural conservation. Policy-makers, urban planners, architects, and conservationist not only need answers to these questions but also need to practice them in a predictable, transparent, spatially explicit and inexpensive manner.

Keywords: architectural and cultural conservation, geomatics, GIS, remote sensing

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Authors: Ming-Yen Chang, Sheng-Hung Ke

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This research focuses on the development of a speed bump identification system for real-time control of adjustable shock absorbers in vehicular suspension systems. The study initially involved the collection of images of various speed bumps, and rubber speed bump profiles found on roadways. These images were utilized for training and recognition purposes through the deep learning object detection algorithm YOLOv5. Subsequently, the trained speed bump identification program was integrated with an in-vehicle camera system for live image capture during driving. These images were instantly transmitted to a computer for processing. Using the principles of monocular vision ranging, the distance between the vehicle and an approaching speed bump was determined. The appropriate control distance was established through both practical vehicle measurements and theoretical calculations. Collaboratively, with the electronically adjustable shock absorbers equipped in the vehicle, a shock absorber control system was devised to dynamically adapt the damping force just prior to encountering a speed bump. This system effectively mitigates passenger discomfort and enhances ride quality.

Keywords: adjustable shock absorbers, image recognition, monocular vision ranging, ride

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Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Rafik Baouche, Rabah Chaouchi

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This work reports the first comprehensive stress field interpretation from the eleven recently drilled wells in the Berkaoui Basin, Algerian Sahara. A cumulative length of 7000+m acoustic image logs from 06 vertical wells were investigated, and a mean NW-SE (128°-145° N) maximum horizontal stress (SHMax) orientation is inferred from the B-D quality wellbore breakouts. The study integrates log-based approach with the downhole measurements to infer pore pressure, in-situ stress magnitudes. Vertical stress (Sv), interpreted from the bulk-density profiles, has an average gradient of 22.36 MPa/km. The Ordovician and Cambrian reservoirs have a pore pressure gradient of 13.47-13.77 MPa/km, which is more than the hydrostatic pressure regime. A 17.2-18.3 MPa/km gradient of minimum horizontal stress (Shmin) is inferred from the fracture closure pressure in the reservoirs. Breakout widths constrained the SHMax magnitude in the 23.8-26.5 MPa/km range. Subsurface stress distribution in the central Saharan Algeria indicates that the present-day stress field in the Berkaoui Basin is principally strike-slip faulting (SHMax > Sv > Shmin). Inferences are drawn on the regional stress pattern and drilling and reservoir development.

Keywords: stress, imagery, breakouts, sahara

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Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

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The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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Authors: Rajpal Kaur, Pooja Choudhary

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Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods.

Keywords: offline signature verification, offline signature recognition, signatures, SURF features, HMM

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: Yan Yue

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Huang Di Nei Jing (HDNJ) is one of the significant traditional Chinese medicine (TCM) classics which lays the foundation of TCM theory and practice. It is an important work for the world to study the ancient civilizations and medical history of China. Language in HDNJ is highly concise and vague, and notably challenging to translate. This paper investigates the translatability of one particular vagueness in HDNJ: the conceptual vagueness which carries the Chinese philosophical and cultural connotations. The corpora tool Sketch Engine is used to provide potential online contexts and word behaviors. Selected two English translations of HDNJ by TCM practitioner and non-practitioner are used to examine frequency and distribution of linguistic features of the translation. It was found the hypothesis about the universals of translated language (explicitation, normalisation) is true in one translation, but it is on the sacrifice of some original contextual connotations. Transliteration is purposefully used in the second translation to retain the original flavor, which is argued as a violation of the principle of relevance in communication because it yields little contextual effects and demands more processing effort of the reader. The translatability of conceptual vagueness in HDNJ is constrained by source language context and the reader’s cognitive environment.

Keywords: corpus-based translation, translatability, TCM classics, vague language

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Authors: Ching-Sung Lee, Wei-Chou Hsu, Han-Yin Liu, Hung-Hsi Huang, Si-Fu Chen, Yun-Jung Yang, Bo-Chun Chiang, Yu-Chuang Chen, Shen-Tin Yang

Abstract:

AlGaN/GaN high electron mobility transistors (HEMTs) have been intensively studied due to their intrinsic advantages of high breakdown electric field, high electron saturation velocity, and excellent chemical stability. They are also suitable for ultra-violet (UV) photodetection due to the corresponding wavelengths of GaN bandgap. To improve the optical responsivity by decreasing the dark current due to gate leakage problems and limited Schottky barrier heights in GaN-based HEMT devices, various metal-oxide-semiconductor HEMTs (MOS-HEMTs) have been devised by using atomic layer deposition (ALD), molecular beam epitaxy (MBE), metal-organic chemical vapor deposition (MOCVD), liquid phase deposition (LPD), and RF sputtering. The gate dielectrics include MgO, HfO2, Al2O3, La2O3, and TiO2. In order to provide complementary circuit operation, enhancement-mode (E-mode) devices have been lately studied using techniques of fluorine treatment, p-type capper, piezoneutralization layer, and MOS-gate structure. This work reports an Al2O3-dielectric Al0.25Ga0.75N/GaN E-mode MOS-HEMT design by using a cost-effective ozone water oxidization technique. The present ozone oxidization method advantages of low cost processing facility, processing simplicity, compatibility to device fabrication, and room-temperature operation under atmospheric pressure. It can further reduce the gate-to-channel distance and improve the transocnductance (gm) gain for a specific oxide thickness, since the formation of the Al2O3 will consume part of the AlGaN barrier at the same time. The epitaxial structure of the studied devices was grown by using the MOCVD technique. On a Si substrate, the layer structures include a 3.9 m C-doped GaN buffer, a 300 nm GaN channel layer, and a 5 nm Al0.25Ga0.75N barrier layer. Mesa etching was performed to provide electrical isolation by using an inductively coupled-plasma reactive ion etcher (ICP-RIE). Ti/Al/Au were thermally evaporated and annealed to form the source and drain ohmic contacts. The device was immersed into the H2O2 solution pumped with ozone gas generated by using an OW-K2 ozone generator. Ni/Au were deposited as the gate electrode to complete device fabrication of MOS-HEMT. The formed Al2O3 oxide thickness 7 nm and the remained AlGaN barrier thickness is 2 nm. A reference HEMT device has also been fabricated in comparison on the same epitaxial structure. The gate dimensions are 1.2 × 100 µm 2 with a source-to-drain spacing of 5 μm for both devices. The dielectric constant (k) of Al2O3 was characterized to be 9.2 by using C-V measurement. Reduced interface state density after oxidization has been verified by the low-frequency noise spectra, Hooge coefficients, and pulse I-V measurement. Improved device characteristics at temperatures of 300 K-450 K have been achieved for the present MOS-HEMT design. Consequently, Al2O3-dielectric Al0.25Ga0.75N/GaN E-mode MOS-HEMTs by using the ozone water oxidization method are reported. In comparison with a conventional Schottky-gate HEMT, the MOS-HEMT design has demonstrated excellent enhancements of 138% (176%) in gm, max, 118% (139%) in IDS, max, 53% (62%) in BVGD, 3 (2)-order reduction in IG leakage at VGD = -60 V at 300 (450) K. This work is promising for millimeter-wave integrated circuit (MMIC) and three-terminal active UV photodetector applications.

Keywords: MOS-HEMT, enhancement mode, AlGaN/GaN, passivation, ozone water oxidation, gate leakage

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Authors: Imam Wierawansyah Eltara, Iftitah, Agus Ismail

Abstract:

In the present work, cellulose nanowhiskers (CNW) extracted from coconut husk fibers, were incorporated in polylactide (PLA)-based composites. Prior to the blending, PLA chains were chemically grafted on the surface of CNW to enhance the compatibilization between CNW and the hydrophobic polyester matrix. Ring-opening polymerization of L-lactide was initiated from the hydroxyl groups available at the CNW surface to yield CNW-g-PLA nanohybrids. PLA-based nanocomposites were prepared by melt blending to ensure a green concept of the study thereby limiting the use of organic solvents. The influence of PLA-grafted cellulose nanoparticles on the mechanical and thermal properties of the ensuing nanocomposites was deeply investigated. The thermal behavior and mechanical properties of the nanocomposites were determined using differential scanning calorimetry (DSC) and dynamical mechanical and thermal analysis (DMTA), respectively. In theory, evidenced that the chemical grafting of CNW enhances their compatibility with the polymeric matrix and thus improves the final properties of the nanocomposites. Large modification of the crystalline properties such as the crystallization half-time was evidenced according to the nature of the PLA matrix and the content of nanofillers.

Keywords: cellulose nanowhiskers, nanocomposites, coconut husk fiber, ring opening polymerization

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Authors: Shahriar Farzam, Maryam Rastgarpour

Abstract:

Image denoising plays extremely important role in digital image processing. Enhancement of clinical image research based on Curvelet has been developed rapidly in recent years. In this paper, we present a method for image contrast enhancement for cone beam CT (CBCT) images based on fast discrete curvelet transforms (FDCT) that work through Unequally Spaced Fast Fourier Transform (USFFT). These transforms return a table of Curvelet transform coefficients indexed by a scale parameter, an orientation and a spatial location. Accordingly, the coefficients obtained from FDCT-USFFT can be modified in order to enhance contrast in an image. Our proposed method first uses a two-dimensional mathematical transform, namely the FDCT through unequal-space fast Fourier transform on input image and then applies thresholding on coefficients of Curvelet to enhance the CBCT images. Consequently, applying unequal-space fast Fourier Transform leads to an accurate reconstruction of the image with high resolution. The experimental results indicate the performance of the proposed method is superior to the existing ones in terms of Peak Signal to Noise Ratio (PSNR) and Effective Measure of Enhancement (EME).

Keywords: curvelet transform, CBCT, image enhancement, image denoising

Procedia PDF Downloads 282

Authors: África Vicario, Fernando J. Álvarez, Felipe Parralejo, Fernando Aranda

Abstract:

The irrigation systems of traditional agriculture, such as gravity-fed irrigation, produce a great waste of water because, generally, there is no control over the amount of water supplied in relation to the water needed. The AIRON Project tries to solve this problem by implementing an IoT-based system to sensor the irrigation plots so that the state of the crops and the amount of water used for irrigation can be known remotely. The IoT system consists of a sensor network that measures the humidity of the soil, the weather conditions (temperature, relative humidity, wind and solar radiation) and the irrigation water flow. The communication between this network and a central gateway is conducted by means of long-range wireless communication that depends on the characteristics of the irrigation plot. The main objective of the AIRON project is to deploy an IoT sensor network in two different plots of the irrigation community of Aranjuez in the Spanish region of Madrid. The first plot is 2 km away from the central gateway, so LoRa has been used as the base communication technology. The problem with this plot is the absence of mains electric power, so devices with energy-saving modes have had to be used to maximize the external batteries' use time. An ESP32 SOC board with a LoRa module is employed in this case to gather data from the sensor network and send them to a gateway consisting of a Raspberry Pi with a LoRa hat. The second plot is located 18 km away from the gateway, a range that hampers the use of LoRa technology. In order to establish reliable communication in this case, the long-term evolution (LTE) standard is used, which makes it possible to reach much greater distances by using the cellular network. As mains electric power is available in this plot, a Raspberry Pi has been used instead of the ESP32 board to collect sensor data. All data received from the two plots are stored on a proprietary server located at the irrigation management company's headquarters. The analysis of these data by means of machine learning algorithms that are currently under development should allow a short-term prediction of the irrigation water demand that would significantly reduce the waste of this increasingly valuable natural resource. The major finding of this work is the real possibility of deploying a remote sensing system for irrigated plots by using Commercial-Off-The-Shelf (COTS) devices, easily scalable and adaptable to design requirements such as the distance to the control center or the availability of mains electrical power at the site.

Keywords: internet of things, irrigation water control, LoRa, LTE, smart farming

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Authors: Ana Serafimovic, Karthik Devarajan

Abstract:

Non-negative matrix factorization approximates a high dimensional non-negative matrix V as the product of two non-negative matrices, W and H, and allows only additive linear combinations of data, enabling it to learn parts with representations in reality. It has been successfully applied in the analysis and interpretation of high dimensional data arising in neuroscience, computational biology, and natural language processing, to name a few. The objective of this paper is to assess a hybrid algorithm for non-negative matrix factorization with multiplicative updates. The method aims to minimize the symmetric version of Kullback-Leibler divergence known as intrinsic information and assumes that the noise is signal-dependent and that it originates from an arbitrary distribution from the exponential family. It is a generalization of currently available algorithms for Gaussian, Poisson, gamma and inverse Gaussian noise. We demonstrate the potential usefulness of the new generalized algorithm by comparing its performance to the baseline methods which also aim to minimize symmetric divergence measures.

Keywords: non-negative matrix factorization, dimension reduction, clustering, intrinsic information, symmetric information divergence, signal-dependent noise, exponential family, generalized Kullback-Leibler divergence, dual divergence

Procedia PDF Downloads 232

Authors: M. S. Tajul Islam, Md. Zahangir Alam

Abstract:

To enhance biogas production through anaerobic digestion, the application of various type of pre-treatment method has some limitations in terms of sustainable environmental management. Many studies on pretreatments especially chemical and physical processes are carried out to evaluate the anaerobic digestion for enhanced biogas production. Among the pretreatment methods acid and alkali pre-treatments gained the highest importance. Previous studies have showed that although acid and alkali pretreatment has significant effect on degradation of biomass, these methods have some negative impact on environment due to their hazard in nature while enzymatic pre-treatment is environmentally friendly. One of the constrains to use of enzyme in pretreatment process for biogas production is high cost which is currently focused to reduce cost through fermentation of waste-based media. As such palm oil mill effluent (POME) as an abundant resource generated during palm oil processing at mill is being used a potential fermentation media for enzyme production. This low cost of enzyme could be an alternative to biogas pretreatment process. This review is to focus direct application of enzyme as enzymatic pre-treatment on POME to enhanced production of biogas.

Keywords: POME, enzymatic pre-treatment, biogas, lignocellulosic biomass, anaerobic digestion

Procedia PDF Downloads 535