Search results for: real-time processing

Authors: Zheng Wang, Zhenhong Li, Jon Mills

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Ground-based synthetic aperture radar (GBSAR) represents a powerful remote sensing tool for deformation monitoring towards various geohazards, e.g. landslides, mudflows, avalanches, infrastructure failures, and the subsidence of residential areas. Unlike spaceborne SAR with a fixed revisit period, GBSAR data can be acquired with an adjustable temporal resolution through either continuous or discontinuous operation. However, challenges arise from processing high temporal-resolution continuous GBSAR data, including the extreme cost of computational random-access-memory (RAM), the delay of displacement maps, and the loss of temporal evolution. Moreover, repositioning errors between discontinuous campaigns impede the accurate measurement of surface displacements. Therefore, a versatile package with two complete chains is developed in this study in order to process both continuous and discontinuous GBSAR data and address the aforementioned issues. The first chain is based on a small-baseline subset concept and it processes continuous GBSAR images unit by unit. Images within a window form a basic unit. By taking this strategy, the RAM requirement is reduced to only one unit of images and the chain can theoretically process an infinite number of images. The evolution of surface displacements can be detected as it keeps temporarily-coherent pixels which are present only in some certain units but not in the whole observation period. The chain supports real-time processing of the continuous data and the delay of creating displacement maps can be shortened without waiting for the entire dataset. The other chain aims to measure deformation between discontinuous campaigns. Temporal averaging is carried out on a stack of images in a single campaign in order to improve the signal-to-noise ratio of discontinuous data and minimise the loss of coherence. The temporal-averaged images are then processed by a particular interferometry procedure integrated with advanced interferometric SAR algorithms such as robust coherence estimation, non-local filtering, and selection of partially-coherent pixels. Experiments are conducted using both synthetic and real-world GBSAR data. Displacement time series at the level of a few sub-millimetres are achieved in several applications (e.g. a coastal cliff, a sand dune, a bridge, and a residential area), indicating the feasibility of the developed GBSAR data processing package for deformation monitoring of a wide range of scientific and practical applications.

Keywords: ground-based synthetic aperture radar, interferometry, small baseline subset algorithm, deformation monitoring

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Authors: Gulmira Zhakupova, Tamara Tultabayeva, Aknur Muldasheva, Assem Sagandyk

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The development of technology and production of products with increased biological value based on the use of natural food raw materials are important tasks in the policy of the food market of the Republic of Kazakhstan. For Kazakhstan, livestock farming, in particular sheep farming, is the most ancient and developed industry and way of life. The history of the Kazakh people is largely connected with this type of agricultural production, with established traditions using dairy products from sheep's milk. Therefore, the development of new technologies from sheep’s milk remains relevant. In addition, one of the most promising areas for the development of food technology for therapeutic and prophylactic purposes is sheep milk products as a source of protein, immunoglobulins, minerals, vitamins, and other biologically active compounds. This article presents the results of research on the study of milk processing technology. The objective of the study is to study the possibilities of processing sheep milk and its role in human nutrition, as well as the results of research to improve the technology of sheep milk products. The studies were carried out on the basis of sanitary and hygienic requirements for dairy products in accordance with the following test methods. To perform microbiological analysis, we used the method for identifying Salmonella bacteria (Horizontal method for identifying, counting, and serotyping Salmonella) in a certain mass or volume of product. Nutritional value is a complex of properties of food products that meet human physiological needs for energy and basic nutrients. The protein mass fraction was determined by the Kjeldahl method. This method is based on the mineralization of a milk sample with concentrated sulfuric acid in the presence of an oxidizing agent, an inert salt - potassium sulfate, and a catalyst - copper sulfate. In this case, the amino groups of the protein are converted into ammonium sulfate dissolved in sulfuric acid. The vitamin composition was determined by HPLC. To determine the content of mineral substances in the studied samples, the method of atomic absorption spectrophotometry was used. The study identified the technological parameters of sheep milk products and determined the prospects for researching sheep milk products. Microbiological studies were used to determine the safety of the study product. According to the results of the microbiological analysis, no deviations from the norm were identified. This means high safety of the products under study. In terms of nutritional value, the resulting products are high in protein. Data on the positive content of amino acids were also obtained. The results obtained will be used in the food industry and will serve as recommendations for manufacturers.

Keywords: dairy, milk processing, nutrition, colostrum

Procedia PDF Downloads 57

Authors: D. Shakirah Shukor, A. Aminudin, Hashim U. A., Waziralilah N. Fathiah, T. Vikneshvaran

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The interior noise control is essential to be explored due to the interior acoustic analysis is significant in the systems such as automobiles, aircraft, air-handling system and diesel engine exhausts system. In this research, experimental work was undertaken for canceling an active noise in the rectangular enclosure. The rectangular enclosure was fabricated with multiple speakers and microphones inside the enclosure. A software program using digital signal processing is implemented to evaluate the proposed method. Experimental work was conducted to obtain the acoustic behavior and characteristics of the rectangular enclosure and noise cancellation based on active noise control in low-frequency range. Noise is generated by using multispeaker inside the enclosure and microphones are used for noise measurements. The technique for noise cancellation relies on the principle of destructive interference between two sound fields in the rectangular enclosure. One field is generated by the original or primary sound source, the other by a secondary sound source set up to interfere with, and cancel, that unwanted primary sound. At the end of this research, the result of output noise before and after cancellation are presented and discussed. On the basis of the findings presented in this research, an active noise cancellation in the rectangular enclosure is worth exploring in order to improve the noise control technologies.

Keywords: active noise control, digital signal processing, noise cancellation, rectangular enclosure

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Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: K. Shaji Mon, P. R. John Sreenidhi

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In deep-submicrometer CMOS processes, time-domain resolution of a digital signal is becoming higher than voltage resolution of analog signals. This claim is nowadays pushing toward a new circuit design paradigm in which the traditional analog signal processing is expected to be progressively substituted by the processing of times in the digital domain. Within this novel paradigm, digitally controlled delay lines (DCDL) should play the role of digital-to-analog converters in traditional, analog-intensive, circuits. Digital delay locked loops are highly prevalent in integrated systems.The proposed paper addresses the glitches present in delay circuits along with area,power dissipation and signal integrity.The digitally controlled delay lines(DCDL) under study have been designed in a 90 nm CMOS technology 6 layer metal Copper Strained SiGe Low K Dielectric. Simulation and synthesis results show that the novel circuits exhibit no glitches for dual output coarse DCDL with less power dissipation and consumes less area compared to the glitch free NAND based DCDL.

Keywords: glitch free, NAND-based DCDL, CMOS, deep-submicrometer

Procedia PDF Downloads 245

Authors: Hassan S. Hussein, Rania A. Abul Seoud, Amr M. Refaat

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Scientific researchers face in the analysis of very large data sets that is increasing noticeable rate in today’s and tomorrow’s technologies. Hadoop and Spark are types of software that developed frameworks. Hadoop framework is suitable for many Different hardware platforms. In this research, a scientific Linux cluster for Big Data analysis (SLBD) is presented. SLBD runs open source software with large computational capacity and high performance cluster infrastructure. SLBD composed of one cluster contains identical, commodity-grade computers interconnected via a small LAN. SLBD consists of a fast switch and Gigabit-Ethernet card which connect four (nodes). Cloudera Manager is used to configure and manage an Apache Hadoop stack. Hadoop is a framework allows storing and processing big data across the cluster by using MapReduce algorithm. MapReduce algorithm divides the task into smaller tasks which to be assigned to the network nodes. Algorithm then collects the results and form the final result dataset. SLBD clustering system allows fast and efficient processing of large amount of data resulting from different applications. SLBD also provides high performance, high throughput, high availability, expandability and cluster scalability.

Keywords: big data platforms, cloudera manager, Hadoop, MapReduce

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Authors: Pasan Dunuwila, V. H. L. Rodrigo, Naohiro Goto

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Sri Lanka is one the largest natural rubber (NR) producers of the world, where the NR industry is a major foreign exchange earner. Among the locally manufactured NR products, concentrated latex (CL) and ribbed smoked sheets (RSS) hold a significant position. Furthermore, these products become the foundation for many products utilized by the people all over the world (e.g. gloves, condoms, tires, etc.). Processing of CL and RSS costs a significant amount of material, energy, and workforce. With this background, both manufacturing lines have immensely challenged by waste, low productivity, lack of cost efficiency, rising cost of production, and many environmental issues. To face the above challenges, the adaptation of sustainable manufacturing measures that use less energy, water, materials, and produce less waste is imperative. However, these sectors lack comprehensive studies that shed light on such measures and thoroughly discuss their improvement potentials from both environmental and economic points of view. Therefore, based on a study of three CL and three RSS mills in Sri Lanka, this study deploys sustainable manufacturing techniques and tools to uncover the underlying potentials to improve performances in CL and RSS processing sectors. This study is comprised of three steps: 1. quantification of average material waste, economic losses, and greenhouse gas (GHG) emissions via material flow analysis (MFA), material flow cost accounting (MFCA), and life cycle assessment (LCA) in each manufacturing process, 2. identification of improvement options with the help of Pareto and What-if analyses, field interviews, and the existing literature; and 3. validation of the identified improvement options via the re-execution of MFA, MFCA, and LCA. With the help of this methodology, the economic and environmental hotspots, and the degrees of improvement in both systems could be identified. Results highlighted that each process could be improved to have less waste, monetary losses, manufacturing costs, and GHG emissions. Conclusively, study`s methodology and findings are believed to be beneficial for assuring the sustainable growth not only in Sri Lankan NR processing sector itself but also in NR or any other industry rooted in other developing countries.

Keywords: concentrated latex, natural rubber, ribbed smoked sheets, Sri Lanka

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Authors: Ju-Hong Lee, Ching-Wei Liao

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Adaptive beamforming using antenna array of sensors is useful in the process of adaptively detecting and preserving the presence of the desired signal while suppressing the interference and the background noise. For conventional adaptive array beamforming, we require a prior information of either the impinging direction or the waveform of the desired signal to adapt the weights. The adaptive weights of an antenna array beamformer under a steered-beam constraint are calculated by minimizing the output power of the beamformer subject to the constraint that forces the beamformer to make a constant response in the steering direction. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering operation. In the literature, it is well known that the performance of an adaptive beamformer will be deteriorated by any steering angle error encountered in many practical applications, e.g., the wireless communication systems with massive antennas deployed at the base station and user equipment. Hence, developing effective signal processing techniques to deal with the problem due to steering angle error for array beamforming systems has become an important research work. In this paper, we present an effective signal processing technique for constructing an adaptive beamformer against the steering angle error. The proposed array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. Based on the presumed steering vector and a preset angle range for steering mismatch tolerance, we first create a matrix related to the direction vector of signal sources. Two projection matrices are generated from the matrix. The projection matrix associated with the desired signal information and the received array data are utilized to iteratively estimate the actual direction vector of the desired signal. The estimated direction vector of the desired signal is then used for appropriately finding the quiescent weight vector. The other projection matrix is set to be the signal blocking matrix required for performing adaptive beamforming. Accordingly, the proposed beamformer consists of adaptive quiescent weights and partially adaptive weights. Several computer simulation examples are provided for evaluating and comparing the proposed technique with the existing robust techniques.

Keywords: adaptive beamforming, robustness, signal blocking, steering angle error

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Authors: Aicha Majda, Abdelhamid El Hassani

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Lung CT image segmentation is a prerequisite in lung CT image analysis. Most of the conventional methods need a post-processing to deal with the abnormal lung CT scans such as lung nodules or other lesions. The simplest similarity measure in the standard Graph Cuts Algorithm consists of directly comparing the pixel values of the two neighboring regions, which is not accurate because this kind of metrics is extremely sensitive to minor transformations such as noise or other artifacts problems. In this work, we propose an improved version of the standard graph cuts algorithm based on the Patch-Based similarity metric. The boundary penalty term in the graph cut algorithm is defined Based on Patch-Based similarity measurement instead of the simple intensity measurement in the standard method. The weights between each pixel and its neighboring pixels are Based on the obtained new term. The graph is then created using theses weights between its nodes. Finally, the segmentation is completed with the minimum cut/Max-Flow algorithm. Experimental results show that the proposed method is very accurate and efficient, and can directly provide explicit lung regions without any post-processing operations compared to the standard method.

Keywords: graph cuts, lung CT scan, lung parenchyma segmentation, patch-based similarity metric

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Authors: Simiksha Balkissoon, Jerome Andrew, Bruce Sithole

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Approximately half of the wood processed in the Forestry, Timber, Pulp and Paper (FTPP) sector is accumulated as waste. The concept of a “green economy” encourages industries to employ revolutionary, transformative technologies to eliminate waste generation by exploring the development of new value chains. The transition towards an almost paperless world driven by the rise of digital media has resulted in a decline in traditional paper markets, prompting the FTTP sector to reposition itself and expand its product offerings by unlocking the potential of value-adding opportunities from renewable resources such as wood to generate revenue and mitigate its environmental impact. The production of valuable products from wood waste such as sawdust has been extensively explored in recent years. Wood components such as lignin, cellulose and hemicelluloses, which can be extracted selectively by chemical processing, are suitable candidates for producing numerous high-value products. In this study, a novel approach to produce high-value cellulose products, such as dissolving wood pulp (DWP), from sawdust was developed. DWP is a high purity cellulose product used in several applications such as pharmaceutical, textile, food, paint and coatings industries. The proposed approach demonstrates the potential to eliminate several complex processing stages, such as pulping and bleaching, which are associated with traditional commercial processes to produce high purity cellulose products such as DWP, making it less chemically energy and water-intensive. The developed process followed the path of experimentally designed lab tests evaluating typical processing conditions such as residence time, chemical concentrations, liquid-to-solid ratios and temperature, followed by the application of suitable purification steps. Characterization of the product from the initial stage was conducted using commercially available DWP grades as reference materials. The chemical characteristics of the products thus far have shown similar properties to commercial products, making the proposed process a promising and viable option for the production of DWP from sawdust.

Keywords: biomass, cellulose, chemical treatment, dissolving wood pulp

Procedia PDF Downloads 186

Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Asma Salman, Brian Gabbitas, Peng Cao, Deliang Zhang

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The performance of a coating is strongly dependent upon its microstructure, which in turn is dependent on the characteristics of the feedstock powder. This study involves the evaluation and performance of a titanium-based composite coating produced by the HVOF (high-velocity oxygen fuel) spraying method. The feedstock for making the composite coating was produced using high energy mechanical milling of TiO2 and Al powders followed by a combustion reaction. The characteristics of the feedstock powder were improved by treating it with an organic binder. Two types of coatings were produced using treated and untreated feedstock powders. The microstructures and characteristics of both types of coatings were studied, and their thermal shock resistance was accessed by dipping into molten aluminum. The results of this study showed that feedstock treatment did not have a significant effect on the microstructure of the coatings. However, it did affect the uniformity, thickness and surface roughness of the coating on the steel substrate. A coating produced by an untreated feedstock showed better thermal shock resistance in molten aluminum compared with the one produced by PVA (polyvinyl alcohol) treatment.

Keywords: coating, feedstock, powder processing, thermal shock resistance, thermally spraying

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: Ravi Kumar

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Agricultural wastes are the non-product outcomes of agricultural processing whose monetary value is less as compared to its collection cost, transportation, and processing. When such agricultural waste is not properly disposed of, it may damage the natural environment and cause detrimental pollution in the atmosphere. Agricultural development and intensive farming methods usually result in wastes that remarkably affect the rural environments in particular and the global environment in general. Agricultural waste has toxicity latent to human beings, animals, and plants through various indirect and direct outlets. The present paper explores the various activities that result in agricultural waste and the routes that can utilize the agricultural waste in a manageable manner to reduce its adverse impact on the environment. Presently, the agricultural waste management system for ecological agriculture and sustainable development has emerged as a crucial issue for policymakers. There is an urgent need to consider agricultural wastes as prospective resources rather than undesirable in order to avoid the transmission and contamination of water, land, and air resources. Waste management includes the disposal and treatment of waste with a view to eliminate threats of waste by modifying the waste to condense the microbial load. The study concludes that proper waste utilization and management will facilitate the purification and development of the ecosystem and provide feasible biofuel resources. This proper utilization and management of these wastes for agricultural production may reduce their accumulation and further reduce environmental pollution by improving environmental health.

Keywords: agricultural waste, utilization, management, environment, health

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Authors: William Adzawla

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In the midst of climate variability and change, the role of gender in ensuring food production remains vital. Therefore, this study analysed the gendered productivity among maize farmers, and the effects of climate change and variability as well as livelihood diversification on gendered productivity gap. This involved a total of 619 farmers selected through a multistage sampling procedure. The data was analysed using Oaxaca Blinder decomposition model. From the result, there is a significant productivity gap of 58.8% and 44.8% between male and female heads, and between male heads and female spouses, respectively. About 87.47% and 98.08% of the variations in gendered productivity were explained by resource endowment. While livelihood diversification significantly influenced gendered productivity through endowment and coefficient effect, climate variables significantly affect productivity gap through only coefficient effects. The study concluded that there is a substantial gendered productivity gap among farmers and this is particularly due to differences in endowment. Generally, there is a high potential of reducing gendered productivity gaps through the provision of equal diversification opportunities and reducing females’ vulnerability to climate change. Among the livelihood activities, off-farm activities such as agro-processing and shea butter processing should be promoted. Similarly, the adoption of on-farm adaptation strategies should be promoted among the farmers.

Keywords: climate change and variability, gender, livelihood diversification, oaxaca-blinder decomposition, productivity gap

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Authors: Markus Remm, Sebastian Dienert

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Vehicle interiors are not only changing in terms of design and functionality but also due to new driving situations in which, for example, autonomous operating modes are possible. Flexible seating positions are changing the requirements for passive safety system behavior and location in the interior of a vehicle. With fully autonomous driving, the driver can, for example, leave the position behind the steering wheel and take a seated position facing backward. Since autonomous and non-autonomous vehicles will share the same road network for the foreseeable future, accidents cannot be avoided, which makes the use of passive safety systems indispensable. With JENOPTIK-VOTAN® A technology, the trend towards flexible predetermined airbag weakening lines is enabled. With the help of laser beams, the predetermined weakening lines are introduced from the backside of the components so that they are absolutely invisible. This machining process is sensor-controlled and guarantees that a small residual wall thickness remains for the best quality and reliability for airbag weakening lines. Due to the wide processing range of the laser, the processing of almost all materials is possible. A CO₂ laser is used for many plastics, natural fiber materials, foams, foils and material composites. A femtosecond laser is used for natural materials and textiles that are very heat-sensitive. This laser type has extremely short laser pulses with very high energy densities. Supported by a high-precision and fast movement of the laser beam by a laser scanner system, the so-called cold ablation is enabled to predetermine weakening lines layer by layer until the desired residual wall thickness remains. In that way, for example, genuine leather can be processed in a material-friendly and process-reliable manner without design implications to the components A-Side. Passive safety in the vehicle is increased through the interaction of modern airbag technology and high-precision laser airbag weakening. The JENOPTIK-VOTAN® A product family has been representing this for more than 25 years and is pointing the way to the future with new and innovative technologies.

Keywords: design freedom, interior material processing, laser technology, passive safety

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Authors: Violina R. Angelova, Radka V. Ivanova, Givko M. Todorov, Krasimir I. Ivanov

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A field study was conducted to evaluate the efficacy of Salvia sclarea L. for phytoremediation of contaminated soils. The experiment was performed on an agricultural fields contaminated by the Non-Ferrous-Metal Works near Plovdiv, Bulgaria. The content of heavy metals in different parts of Salvia sclarea L. (roots, stems, leaves and inflorescences) was determined by ICP. The essential oil of the Salvia sclarea L. was obtained by steam distillation in laboratory conditions and was analyzed for heavy metals and its chemical composition was determined. Salvia sclarea L. is a plant which is tolerant to heavy metals and can be grown on contaminated soils. Based on the obtained results and using the most common criteria, Salvia sclarea L. can be classified as Pb hyperaccumulator and Cd and Zn accumulators, therefore, this plant has suitable potential for the phytoremediation of heavy metal contaminated soils. Favorable is also the fact that heavy metals do not influence the development of the Salvia sclarea L., as well as on the quality and quantity of the essential oil. For clary sage oil obtained from the processing of clary sage grown on highly contaminated soils, its key odour-determining ingredients meet the quality requirements of the European Pharmacopoeia and BS ISO 7609 regarding Bulgarian clary sage oil and/or have values that are close to the limits of these standards. The possibility of further industrial processing will make Salvia sclarea L. an economically interesting crop for farmers of phytoextraction technology.

Keywords: clary sage, heavy metals, phytoremediation, polluted soils

Procedia PDF Downloads 216

Authors: Sungging Pintowantoro, Yuli Setiyorini, Rochman Rochim, Agung Purniawan

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The bacterial infections are frequent and undesired occurrences after bone fracture treatment. One approach to reduce the incidence of bone fracture infection is the additional of microbial agents into bone cement. In this study, the synthesis of bone cement from re-cycles biowaste was successfully conducted completed with anti-bacterial function. The re-cycle of biowaste using microwave assisted was done in our previous studies in order to produce some of powder (calcium carbonate, carbonated-hydroxyapatite and chitosan). The ratio of these powder combined with methylmethacrylate (MMA) as the matrix in bone cement were investigated using XRD, FTIR, SEM-EDX, hardness test and anti-bacterial test, respectively. From the XRD, FTIR and EDX were resulted the formation of carbonated-hydroxyapatite, calcium carbonate and chitosan. The morphology was revealed porous structure both C2H3K1L and C2H1K3L, respectively. The antibacterial activity was tested against Staphylococcus aureus (S. aureus) for 24 hours. The inhibition of S. aureus was clearly shown, the hollow zone was resulted in various distance 14.2mm, 7.5mm, and 7.7mm, respectively. The hardness test was depicted in various results, however, C2H1K3L can be achived 36.84HV which is closed to dry cancelous bone 35HV. In general, this study results was promising materials to use as bone cement materials.

Keywords: biomaterials, biowaste recycling, materials processing, microwave processing

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Authors: Ting Wu, Feiting Hu, Xinyue Zhang, Shuxin Tang, Xiaoyun Xu

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As a by-product of yuba, processing wastewater of Yuba (PWY) contains many bioactive components such as soybean isoflavones, soybean polysaccharides and soybean oligosaccharides, which is a good source of prebiotics and has a potential of high value utilization. The use of Lactobacillus plantarum to ferment PWY can be considered as a potential biogenic element, which can regulate the balance of intestinal microbiota. In this study, firstly, Lactobacillus plantarum was used to ferment PWY to improve its content of active components and antioxidant activity. Then, the health effect of fermented processing wastewater of yuba (FPWY) was measured in vitro. Finally, microencapsulation technology was used applied to improve the sustained release of FPWY and reduce the loss of active components in the digestion process, as well as to improving the activity of FPWY. The main results are as follows: (1) FPWY presented a good antioxidant capacity with DPPH free radical scavenging ability (0.83 ± 0.01 mmol Trolox/L), ABTS free radical scavenging ability (7.47 ± 0.35 mmol Trolox/L) and iron ion reducing ability (1.11 ± 0.07 mmol Trolox/L). Compared with non-fermented processing wastewater of yuba (NFPWY), there was no significant difference in the content of total soybean isoflavones, but the content of glucoside soybean isoflavones decreased, and aglyconic soybean isoflavones increased significantly. After fermentation, PWY can effectively reduce the soluble monosaccharides, disaccharides and oligosaccharides, such as glucose, fructose, galactose, trehalose, stachyose, maltose, raffinose and sucrose. (2) FPWY can significantly enhance the growth of beneficial bacteria such as Bifidobacterium, Ruminococcus and Akkermansia, significantly inhibit the growth of harmful bacteria E.coli, regulate the structure of intestinal microbiota, and significantly increase the content of short-chain fatty acids such as acetic acid, propionic acid, butyric acid, isovaleric acid. Higher amount of lactic acid in the gut can be further broken down into short chain fatty acids. (3) In order to improve the stability of soybean isoflavones in FPWY during digestion, sodium alginate and chitosan were used as wall materials for embedding. The FPWY freeze-dried powder was embedded by the method of acute-coagulation bath. The results show that when the core wall ratio is 3:1, the concentration of chitosan is 1.5%, the concentration of sodium alginate is 2.0%, and the concentration of calcium is 3%, the embossing rate is 53.20%. In the simulated in vitro digestion stage, the release rate of microcapsules reached 59.36% at the end of gastric digestion and 82.90% at the end of intestinal digestion. Therefore, the core materials with good sustained-release performance of microcapsules were almost all released. The structural analysis results of FPWY microcapsules show that the microcapsules have good mechanical properties. Its hardness, springness, cohesiveness, gumminess, chewiness and resilience were 117.75± 0.21 g, 0.76±0.02, 0.54±0.01, 63.28±0.71 g·sec, 48.03±1.37 g·sec, 0.31±0.01, respectively. Compared with the unembedded FPWY, the infrared spectrum results showed that the microcapsules had embedded effect on the FPWY freeze-dried powder.

Keywords: processing wastewater of yuba, lactobacillus plantarum, intestinal microbiota, microcapsule

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Authors: McKenzie S. Braley, Lesley Jessiman

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If language reveals internal psychological processing, then it is also likely that language use in autobiographical memory transcripts may be used as a window into attachment style and related personality features. The current study, therefore, examined the possible associations between attachment style, negative affectivity, social inhibition, and linguistic features extracted from autobiographical memory transcripts. Young adult participants (n = 61) filled out attachment and personality questionnaires, and orally reported a relationship-related memory. Memories were audio-recorded and later transcribed verbatim. Using a computerized linguistic extraction tool, positive affect words, negative affect words, and cognition words were extracted. Spearman’s rank correlation coefficients revealed that attachment anxiety was negatively correlated with cognition words (r2 = -0.26, p = 0.047) and that negative affectivity was negatively correlated with positive affect words (r2 = -0.32, p = 0.012). The findings suggest that attachment style and personality are associated with speech styles indicative of both emotionality and depth of processing. Because attachment styles, negative affectivity, and social inhibition are associated with poor mental health outcomes, analyses of key linguistics features in autobiographical memory narratives may provide reliable screening tools for mental wellbeing.

Keywords: attachment style, autobiographical memory, language, negative affectivity, social inhibition

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Authors: Joanna Wielochowska, Aneta Wielochowska

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Whilst the relationship between scientific areas such as cognitive psychology, neurobiology and philosophy of mind has been emphasized in recent decades of scientific research, concepts and discoveries made in both fields overlap and complement each other in their quest for answers to similar questions. The object of the following case study is to describe, analyze and illustrate the nature and characteristics of a certain cognitive experience which appears to display features of synaesthesia, or rather high-level synaesthesia (ideasthesia). The following research has been conducted on the subject of two authors, monozygotic twins (both polysynaesthetes) experiencing involuntary associations of identical nature. Authors made attempts to identify which cognitive and conceptual dependencies may guide this experience. Operating on self-introduced nomenclature, the described phenomenon- multi-dimensional processing of textual and visual information- aims to define a relationship that involuntarily and immediately couples the content introduced by means of text or image a sensation of appearing in a certain place in the mind’s eye. More precisely: (I) defining a concept introduced by means of textual content during activity of reading or writing, or (II) defining a concept introduced by means of visual content during activity of looking at image(s) with simultaneous sensation of being allocated to a given place in the mind’s eye. A place can be then defined as a cognitive representation of a certain concept. During the activity of processing information, a person has an immediate and involuntary feel of appearing in a certain place themselves, just like a character of a story, ‘observing’ a venue or a scenery from one or more perspectives and angles. That forms a unique and unified experience, constituting a background mental landscape of text or image being looked at. We came to a conclusion that semantic allocations to a given place could be divided and classified into the categories and subcategories and are naturally linked with an individual’s semantic knowledge-base. A place can be defined as a representation one’s unique idea of a given concept that has been established in their semantic knowledge base. A multi-level structure of selectivity of places in the mind’s eye, as a reaction to a given information (one stimuli), draws comparisons to structures and patterns found in botany. Double-flowered varieties of flowers and a whorl system (arrangement) which is characteristic to components of some flower species were given as an illustrative example. A composition of petals that fan out from one single point and wrap around a stem inspired an idea that, just like in nature, in philosophy of mind there are patterns driven by the logic specific to a given phenomenon. The study intertwines terms perceived through the philosophical lens, such as definition of meaning, subjectivity of meaning, mental atmosphere of places, and others. Analysis of this rare experience aims to contribute to constantly developing theoretical framework of the philosophy of mind and influence the way human semantic knowledge base and processing given content in terms of distinguishing between information and meaning is researched.

Keywords: information and meaning, information processing, mental atmosphere of places, patterns in nature, philosophy of mind, selectivity, semantic knowledge base, senses, synaesthesia

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Authors: Tobias Schmithuesen, Johannes Henrich Schleifenbaum

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The additive manufacturing process laser powder bed fusion offers many advantages over conventional manufacturing processes. For example, almost any complex part can be produced, such as topologically optimized lightweight parts, which would be inconceivable with conventional manufacturing processes. A major challenge posed by the LPBF process, however, is, in most cases, the need to use and remove support structures on critically inclined part surfaces (α < 45 ° regarding substrate plate). These are mainly used for dimensionally accurate mapping of part contours and to reduce distortion by absorbing process-related internal stresses. Furthermore, they serve to transfer the process heat to the substrate plate and are, therefore, indispensable for the LPBF process. A major challenge for the economical use of the LPBF process in industrial process chains is currently still the high manual effort involved in removing support structures. According to the state of the art (SoA), the parts are usually treated by simple hand tools (e.g., pliers, chisels) or by machining (e.g., milling, turning). New automatable approaches are the removal of support structures by means of wet chemical ablation and thermal deburring. According to the state of the art, the support structures are essentially adapted to the LPBF process and not to potential post-processing steps. The aim of this study is the determination of support structure designs that are adapted to the mentioned post-processing approaches. In the first step, the essential boundary conditions for complete removal by means of the respective approaches are identified. Afterward, a representative demonstrator part with various macroscopic support structure designs will be LPBF-manufactured and tested with regard to a complete powder and support removability. Finally, based on the results, potentially suitable support structure designs for the respective approaches will be derived. The investigations are carried out on the example of the aluminum alloy AlSi10Mg.

Keywords: additive manufacturing, laser powder bed fusion, laser beam melting, selective laser melting, post processing, tool-free, wet chemical ablation, thermal deburring, aluminum alloy, AlSi10Mg

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Authors: Shubhangi Salokhe

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Agriculture encompasses crop and livestock production, forestry, and fisheries for food and non-food products. Farmers combine land, water, commercial inputs, labor, and their management skills into practices and systems that produce food and fibre. Harvesting of agricultural produce is either followed by the processing of fresh produce or storage for later consumption. All these activities result in a vast generation of waste in terms of crop residue or food waste. So, a large amount of agricultural waste is produced every year. Waste arising from food and agricultural sectors has the potential for vast applications. So, agricultural waste management is an essential component of sustainable agriculture. The major portion of the waste comes from the residues of crops on farms, food processing, livestock, aquaculture, and agro-industry waste. Therefore, management of these agricultural wastes is an important task, and it requires robust strategic planning. It can contribute to three pillars of sustainable agriculture development. It protects the environment (environmental pillar), enhances the livelihoods of farmers (economic pillar), and can contribute to increasing the sustainability of the agricultural sector (social pillar). This paper addresses the essential technological aspects, possible solutions, and sound policy concerns to accomplish long-term way out of agriculture waste management and to minimize the negative impact of waste on the environment. The author has developed a sustainable agriculture waste management model for improving the sustainability of agriculture.

Keywords: agriculture, development, management, waste

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

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Authors: Paris Binos

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The present work investigates developmental aspects of the duration of isolated vowels in infants with normal hearing compared to those who received cochlear implants (CIs) before two years of age. Infants with normal hearing produced shorter vowel duration since this find related with more mature production abilities. First isolated vowels are transparent during the protophonic stage as evidence of an increased motor and linguistic control. Vowel duration is a crucial factor for the transition of prelexical speech to normal adult speech. Despite current knowledge of data for infants with normal hearing more research is needed to unravel productions skills in early implanted children. Thus, isolated vowel productions by two congenitally hearing-impaired Greek infants (implantation ages 1:4-1:11; post-implant ages 0:6-1:3) were recorded and sampled for six months after implantation with a Nucleus-24. The results compared with the productions of three normal hearing infants (chronological ages 0:8-1:1). Vegetative data and vocalizations masked by external noise or sounds were excluded. Participants had no other disabilities and had unknown deafness etiology. Prior to implantation the infants had an average unaided hearing loss of 95-110 dB HL while the post-implantation PTA decreased to 10-38 dB HL. The current research offers a methodology for the processing of the prelinguistic productions based on a combination of acoustical and auditory analyses. Based on the current methodological framework, duration measured through spectrograms based on wideband analysis, from the voicing onset to the end of the vowel. The end marked by two co-occurring events: 1) The onset of aperiodicity with a rapid change in amplitude in the waveform and 2) a loss in formant’s energy. Cut-off levels of significance were set at 0.05 for all tests. Bonferroni post hoc tests indicated that difference was significant between the mean duration of vowels of infants wearing CIs and their normal hearing peers. Thus, the mean vowel duration of CIs measured longer compared to the normal hearing peers (0.000). The current longitudinal findings contribute to the existing data for the performance of children wearing CIs at a very young age and enrich also the data of the Greek language. The above described weakness for CI’s performance is a challenge for future work in speech processing and CI’s processing strategies.

Keywords: cochlear implant, duration, spectrogram, vowel

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Authors: Atheer Al-Maqbali, Mohammed Al-Rizeiqi, Pankaj Pathare

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There is an ever-increased demand for the consumption of banana products in Oman and elsewhere in the region due to the nutritional value and the decent taste of the product. There are approximately 3,751 acres of land designated for banana cultivation in the Sultanate of Oman, which produces approximately 18,447 tons of banana product. The fresh banana product is extremely perishable, resulting in a significant post-harvest economic loss. Since the product has high sensory acceptability, the drying method is a common method for processing fresh banana products. This study aims to use the drying technology in the production of dried bananas to preserve the largest amount of natural color and delicious taste for the consumer. The study also aimed to assess the shelf stability of both water activity (aw) and color (L*, a*, b*) for fresh and finished dried bananas by using a Conventional Air Drying System. Water activity aw, color characteristic L a b, and product’s hardness were analyzed for 3mm, 5mm, and7 mm thickness at different temperaturesoC. All data were analyzed statistically using STATA 13.0, and α ≤ 0.05 was considered for the significance level. The study is useful to banana farmers to improve cultivation, food processors to optimize producer’s output and policy makers in the optimization of banana processing and post-harvest management of the products.

Keywords: banana, drying, oman, quality, thickness, hardness, color

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of liver and spleen from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of ROI from CT images is usually a difficult task. This difficulty is the gray’s level of which is similar to the other organ also the ROI are connected to the ribs, heart, kidneys, etc. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the surrounding and connected organs and tissues by applying morphological filters. This first step makes the extraction of interest regions easier. The second step consists of improving the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce these deficiencies by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: Rowan P. Martnishn

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During the execution of a National Endowment of the Arts grant, approximately 55 interviews were collected from professionals across various fields. These interviews were used to create deliverables – historical connections for creations that began as art and evolved entirely into computing technology. With dozens of hours’ worth of transcripts to be analyzed by qualitative coders, a quantitative methodology was created to sift through the documents. The initial step was to both clean and format all the data. First, a basic spelling and grammar check was applied, as well as a Python script for normalized formatting which used an open-source grammatical formatter to make the data as coherent as possible. 10 documents were randomly selected to manually review, where words often incorrectly translated during the transcription were recorded and replaced throughout all other documents. Then, to remove all banter and side comments, the transcripts were spliced into paragraphs (separated by change in speaker) and all paragraphs with less than 300 characters were removed. Secondly, a keyword extractor, a form of natural language processing where significant words in a document are selected, was run on each paragraph for all interviews. Every proper noun was put into a data structure corresponding to that respective interview. From there, a Bidirectional and Auto-Regressive Transformer (B.A.R.T.) summary model was then applied to each paragraph that included any of the proper nouns selected from the interview. At this stage the information to review had been sent from about 60 hours’ worth of data to 20. The data was further processed through light, manual observation – any summaries which proved to fit the criteria of the proposed deliverable were selected, as well their locations within the document. This narrowed that data down to about 5 hours’ worth of processing. The qualitative researchers were then able to find 8 more connections in addition to our previous 4, exceeding our minimum quota of 3 to satisfy the grant. Major findings of the study and subsequent curation of this methodology raised a conceptual finding crucial to working with qualitative data of this magnitude. In the use of artificial intelligence there is a general trade off in a model between breadth of knowledge and specificity. If the model has too much knowledge, the user risks leaving out important data (too general). If the tool is too specific, it has not seen enough data to be useful. Thus, this methodology proposes a solution to this tradeoff. The data is never altered outside of grammatical and spelling checks. Instead, the important information is marked, creating an indicator of where the significant data is without compromising the purity of it. Secondly, the data is chunked into smaller paragraphs, giving specificity, and then cross-referenced with the keywords (allowing generalization over the whole document). This way, no data is harmed, and qualitative experts can go over the raw data instead of using highly manipulated results. Given the success in deliverable creation as well as the circumvention of this tradeoff, this methodology should stand as a model for synthesizing qualitative data while maintaining its original form.

Keywords: B.A.R.T.model, keyword extractor, natural language processing, qualitative coding

Procedia PDF Downloads 30