Search results for: adaptive histogram equalization

Authors: Sami M. Alshareef

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In the rapidly evolving energy landscape, the integration of renewable energy sources and the electrification of transportation are essential steps toward achieving sustainability goals. However, these advancements introduce new challenges, particularly in maintaining frequency stability due to variable photovoltaic (PV) generation and the growing demand for fast charging stations. The variability of photovoltaic (PV) generation due to weather conditions can disrupt the balance between generation and load, resulting in frequency deviations. To ensure the stability of power systems, it is imperative to develop effective under frequency load-shedding schemes. This research proposal presents an adaptive under-frequency load shedding scheme based on the power swing equation, designed explicitly for the IEEE-9 Bus Test System, that includes PV generation and fast charging stations. This research aims to address these challenges by developing an advanced scheme that dynamically disconnects fast charging stations based on power imbalances. The scheme prioritizes the disconnection of stations near affected areas to expedite system frequency stabilization. To achieve these goals, the research project will leverage the power swing equation, a widely recognized model for analyzing system dynamics during under-frequency events. By utilizing this equation, the proposed scheme will adaptively adjust the load-shedding process in real-time to maintain frequency stability and prevent power blackouts. The research findings will support the transition towards sustainable energy systems by ensuring a reliable and uninterrupted electricity supply while enhancing the resilience and stability of power systems during under-frequency events.

Keywords: load shedding, fast charging stations, pv generation, power system resilience

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Authors: Richard Wolfel, Amy Richmond

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Urban resilience and vulnerability are critical concepts in the modern city due to the increased sociocultural, political, economic, demographic, and environmental stressors that influence current urban dynamics. Urban scholars need help explaining urban resilience and vulnerability. First, cities are dominated by people, which is challenging to model, both from an explanatory and a predictive perspective. Second, urban regions are highly recursive in nature, meaning they not only influence human action, but the structures of cities are constantly changing due to human actions. As a result, explanatory frameworks must continuously evolve as humans influence and are influenced by the urban environment in which they operate. Finally, modern cities have populations, sociocultural characteristics, economic flows, and environmental impacts on order of magnitude well beyond the cities of the past. As a result, the frameworks that seek to explain the various functions of a city that influence urban resilience and vulnerability must address the complex adaptive nature of cities and the interaction of many distinct factors that influence resilience and vulnerability in the city. This project develops a taxonomy and framework for organizing and explaining urban vulnerability. The framework is built on a well-established political development model that includes six critical classes of urban dynamics: political presence, political legitimacy, political participation, identity, production, and allocation. In addition, the framework explores how environmental security and technology influence and are influenced by the six elements of political development. The framework aims to identify key tipping points in society that act as influential agents of urban vulnerability in a region. This will help analysts and scholars predict and explain the influence of both physical and human geographical stressors in a dense urban area.

Keywords: urban resilience, vulnerability, sociocultural stressors, political stressors

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Authors: Jing-Ma

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Digital transformation (DT) is an important way for manufacturing enterprises to shape new competitive advantages, and how to choose an effective DT strategy is crucial for enterprise growth and sustainable development. Rooted in strategic change theory, this paper incorporates the dimensions of managers' digital cognition, organizational conditions, and external environment into the same strategic analysis framework and integrates the dynamic QCA method and PSM method to study the antecedent grouping of the DT strategy mode of manufacturing enterprises and its impact on corporate profitability based on the data of listed manufacturing companies in China from 2015 to 2019. We find that the synergistic linkage of different dimensional elements can form six equivalent paths of high-level DT, which can be summarized as the proactive change mode of resource-capability dominated as well as adaptive response mode such as industry-guided resource replenishment. Capacity building under complex environments, market-industry synergy-driven, forced adaptation under peer pressure, and the managers' digital cognition play a non-essential but crucial role in this process. Except for individual differences in the market industry collaborative driving mode, other modes are more stable in terms of individual and temporal changes. However, it is worth noting that not all paths that result in high levels of DT can contribute to enterprise profitability, but only high levels of DT that result from matching the optimization of internal conditions with the external environment, such as industry technology and macro policies, can have a significant positive impact on corporate profitability.

Keywords: digital transformation, strategy mode, enterprise profitability, dynamic QCA, PSM approach

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Mobile Grid Computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using different types of electronic portable devices. In a grid environment the security issues are like authentication, authorization, message protection and delegation handled by GSI (Grid Security Infrastructure). Proving better security between mobile devices and grid infrastructure is a major issue, because of the open nature of wireless networks, heterogeneous and distributed environments. In a mobile grid environment, the individual computing devices may be resource-limited in isolation, as an aggregated sum, they have the potential to play a vital role within the mobile grid environment. Some adaptive methodology or solution is needed to solve the issues like authentication of a base station, security of information flowing between a mobile user and a base station, prevention of attacks within a base station, hand-over of authentication information, communication cost of establishing a session key between mobile user and base station, computing complexity of achieving authenticity and security. The sharing of resources of the devices can be achieved only through the trusted relationships between the mobile hosts (MHs). Before accessing the grid service, the mobile devices should be proven authentic. This paper proposes the dynamic certificate based mutual authentication protocol between two mobile hosts in a mobile grid environment. The certificate generation process is done by CA (Certificate Authority) for all the authenticated MHs. Security (because of validity period of the certificate) and dynamicity (transmission time) can be achieved through the secure service certificates. Authentication protocol is built on communication services to provide cryptographically secured mechanisms for verifying the identity of users and resources.

Keywords: mobile grid computing, certificate authority (CA), SSL/TLS protocol, secured service certificates

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Jianan Sun, Ziwen Ye

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Multidimensional computerized adaptive testing (MCAT) is a popular research topic in psychometrics. It is important for practitioners to clearly know the item-trait patterns of administered items when a test like MCAT is operated. Item-trait pattern recognition refers to detecting which latent traits in a psychological test are measured by each of the specified items. If the item-trait patterns of the replenished items in MCAT item pool are well detected, the interpretability of the items can be improved, which can further promote the abilities of the examinees who attending the MCAT to be accurately estimated. This research explores to solve the item-trait pattern recognition problem of the replenished items in MCAT item pool from the perspective of statistical variable selection. The popular multidimensional item response theory model, multidimensional two-parameter logistic model, is assumed to fit the response data of MCAT. The proposed method uses the least absolute shrinkage and selection operator (LASSO) to detect item-trait patterns of replenished items based on the essential information of item responses and ability estimates of examinees collected from a designed MCAT procedure. Several advantages of the proposed method are outlined. First, the proposed method does not strictly depend on the relative order between the replenished items and the selected operational items, so it allows the replenished items to be mixed into the operational items in reasonable order such as considering content constraints or other test requirements. Second, the LASSO used in this research improves the interpretability of the multidimensional replenished items in MCAT. Third, the proposed method can exert the advantage of shrinkage method idea for variable selection, so it can help to check item quality and key dimension features of replenished items and saves more costs of time and labors in response data collection than traditional factor analysis method. Moreover, the proposed method makes sure the dimensions of replenished items are recognized to be consistent with the dimensions of operational items in MCAT item pool. Simulation studies are conducted to investigate the performance of the proposed method under different conditions for varying dimensionality of item pool, latent trait correlation, item discrimination, test lengths and item selection criteria in MCAT. Results show that the proposed method can accurately detect the item-trait patterns of the replenished items in the two-dimensional and the three-dimensional item pool. Selecting enough operational items from the item pool consisting of high discriminating items by Bayesian A-optimality in MCAT can improve the recognition accuracy of item-trait patterns of replenished items for the proposed method. The pattern recognition accuracy for the conditions with correlated traits is better than those with independent traits especially for the item pool consisting of comparatively low discriminating items. To sum up, the proposed data-driven method based on the LASSO can accurately and efficiently detect the item-trait patterns of replenished items in MCAT.

Keywords: item-trait pattern recognition, least absolute shrinkage and selection operator, multidimensional computerized adaptive testing, variable selection

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Authors: Guoqin Ge, Minhui Huang, Yazhou Zhou

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The growth of the Chengdu-Chongqing urban agglomeration has been designated as a national strategy in China. Analyzing its spatial evolution characteristics is crucial for devising relevant development strategies. This paper enhances the gravitational model by using temporal distance as a factor. It applies this improved model to assess the economic interconnection and concentration level of each geographical unit within the Chengdu-Chongqing urban agglomeration between 2011 and 2019. On this basis, this paper examines the spatial correlation characteristics of economic agglomeration intensity and urban-rural development equalization by employing spatial autocorrelation analysis. The study findings indicate that the spatial integration in the Chengdu-Chongqing urban agglomeration is currently in the "point-axis" development stage. The spatial organization structure is becoming more flattened, and there is a stronger economic connection between the core of the urban agglomeration and the peripheral areas. The integration of the Chengdu-Chongqing urban agglomeration is currently hindered by conflicting interests and institutional heterogeneity between Chengdu and Chongqing. Additionally, the connections between the relatively secondary spatial units are largely loose and weak. The strength and scale of economic ties and the level of urban-rural equilibrium among spatial units within the Chengdu-Chongqing urban agglomeration have increased, but regional imbalances have continued to widen, and such positive and negative changes have been characterized by the spatial and temporal synergistic evolution of the "core-periphery". Ultimately, this paper presents planning ideas for the future integration development of the Chengdu-Chongqing urban agglomeration, drawing from the findings.

Keywords: integration, planning strategy, space organization, space evolution, urban agglomeration

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Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

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Authors: Nihad Karim Chowdhury, Mohammad Sanaullah Chowdhury, Muhammed Jamshed Alam Patwary, Rubel Biswas

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In content-based 3D shape retrieval system, achieving high search performance has become an important research problem. A challenging aspect of this problem is to find an effective shape descriptor which can discriminate similar shapes adequately. To address this problem, we propose a new shape descriptor for 3D shape models by combining multi-resolution with multi-block center-symmetric local binary pattern operator. Given an arbitrary 3D shape, we first apply pose normalization, and generate a set of multi-viewed 2D rendered images. Second, we apply Gaussian multi-resolution filter to generate several levels of images from each of 2D rendered image. Then, overlapped sub-images are computed for each image level of a multi-resolution image. Our unique multi-block CS-LBP comes next. It allows the center to be composed of m-by-n rectangular pixels, instead of a single pixel. This process is repeated for all the 2D rendered images, derived from both ‘depth-buffer’ and ‘silhouette’ rendering. Finally, we concatenate all the features vectors into one dimensional histogram as our proposed 3D shape descriptor. Through several experiments, we demonstrate that our proposed 3D shape descriptor outperform the previous methods by using a benchmark dataset.

Keywords: 3D shape retrieval, 3D shape descriptor, CS-LBP, overlapped sub-images

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Authors: Sergey I. Chizhikov, Vladimir Y. Molchanov, Konstantin B. Yushkov

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We demonstrate a method for adaptive spatial shaping of laser beams by means of acousto-optic Bragg diffraction. Transformation of the angular spectrum during Bragg diffraction is used to convert Gaussian intensity distribution into a flat-top one. Theoretical model is supported by the experiment.

Keywords: acousto-optics, flat top, beam shaping, Bragg diffraction

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Authors: T. Garba, T. O. Quddus, Y. Y. Babanyara, M. A. Modibbo

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Desertification is define as the changing of productive land into a desert as the result of ruination of land by man-induced soil erosion, which forces famers in the affected areas to move migrate or encourage into reserved areas in search of a fertile land for their farming activities. This study therefore used remote sensing imageries to determine the level of changes in the vegetative areas. To achieve that Normalized Difference of the Vegetative Index (NDVI), classified imageries and image slicing derived from landsat TM 1986, land sat ETM 1999 and Nigeria sat 1 2007 were used to determine changes in vegetations. From the Classified imageries it was discovered that there a more natural vegetation in classified images of 1986 than that of 1999 and 2007. This finding is also future in the three NDVI imageries, it was discovered that there is increased in high positive pixel value from 0.04 in 1986 to 0.22 in 1999 and to 0.32 in 2007. The figures in the three histogram also indicted that there is increased in vegetative areas from 29.15 Km2 in 1986, to 60.58 Km2 in 1999 and then to 109 Km2 in 2007. The study recommends among other things that there is need to restore natural vegetation through discouraging of farming activities in and around the natural vegetation in the study area.

Keywords: vegetative index, classified imageries, change detection, landsat, vegetation

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Authors: Shree Kumar Maharjan

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Climate change has varied impacts on diverse livelihood sectors, which is more prominent at the community level. The stakeholders and local institutions have been supporting the communities either by building adaptive capacities and resilience or minimizing the impacts of different adaptation interventions. Some of these interventions are effective, whereas others need further dynamisms and exertions considering the complexity of the risks and vulnerabilities. Hence, consolidated efforts of concerned stakeholders are required to minimize and adapt the present and future impacts. This study digs out and analyses the perceptions of local stakeholders in climate change adaptation in Madi and Deukhuri valleys of Nepal through a questionnaire survey. The study has categorized the local stakeholders into 5 groups in the study sites – Farmers groups and cooperatives, Government, I/NGOs, Development banks and education and other organizations. The local stakeholders revealed flood, drought, cold wave and riverbank erosion as the major climatic risks and hazards found in the sites eventually impacting on the loss of agricultural production, loss of agricultural land and properties, loss of livestock, the emergence of diseases and pest. The stakeholders believed that most of the farmers dealing with these impacts based on their traditional knowledge and practices, followed by with the support of NGOs and with the help of neighbors and community. The major supports of the stakeholders to deal with these impacts were on training and awareness, risk analysis and minimization, livelihood improvement, financial support, coordination and networking and facilitation in policy formulation. The stakeholders emphasized primarily on capacity building, appropriate technologies, community-based planning and monitoring, prioritization to the poor and the marginalized and establishment of community fund respectively for building adaptive capacities.

Keywords: climate change adaptation, local stakeholders, Madi, Deukhuri, Nepal

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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

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Authors: Setapo Moloi, Jun-Ichiro Giorgos Tsutsumi

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South Africa’s need for the provision of housing within its major city centres, specifically Gauteng Province (GP), is a major concern. Initiatives for converting misused/ unused buildings to suitable housing for residents who work in the city as well as prospective citizens are currently underway, one aspect that is needed currently, is the re-possession of these buildings repurposing, into housing communities for quality low cost mixed density housing and for this process to have minimal strain on existing infrastructure like energy, emission reduction etc. Unfortunately, there are instances in Johannesburg, the country’s economic capital, with 2017 estimates claiming that 700 buildings lay unused or misused due to issues that will be discussed in this paper, these then become hubs for illegal activity and are an unacceptable form of shelter. It can be argued that the provision of inner-city social housing is lacking, but not due to the unavailability of funding or usable land and buildings, but that these assets are not being used appropriately nor to their full potential. Currently the GP government has mandated the re-purposing of all buildings that meet their criteria (structural stability, feasibility, adaptability, etc.) with the intention of inviting interested parties to propose conversions of the buildings into densified social housing. Going forward, the proposed focus is creation of social housing communities within existing buildings which may be retrofitted with sustainable technologies, green design strategies and principles, aiming for the finished buildings to achieve ‘Net-Zero/Positive’ status. A Net-Zero building, according to The Green Building Council of South Africa (GBCSA) is a building which manages to produce resources it needs to function, and reduces wastage, emissions and demand of these resources during its lifespan. The categories which GBCSA includes are carbon, water, waste and ecology, this may include material selection, construction methods, etc.

Keywords: adaptive reuse, conversion, net-zero, social housing, sustainable communities

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Authors: Ogar Ofut Tumenayu, Olga Shabalina

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This paper describes the application of two types of pedagogical agents’ technology with different functions in an adaptive educational game with the sole aim of improving learning and enhancing interactivities in Digital Educational Games (DEG). This idea could promote the elimination of some problems of DEG, like isolation in game-based learning, by introducing a tutor and tutee pedagogical agents. We present an analysis of a learning companion interacting in a peer tutoring environment as a step toward improving social interactions in the educational game environment. We show that tutor and tutee agents use different interventions and interactive approaches: the tutor agent is engaged in tracking the learner’s activities and inferring the learning state, while the tutee agent initiates interactions with the learner at the appropriate times and in appropriate manners. In order to provide motivation to prevent mistakes and clarity a game task, the tutor agent uses the help dialog tool to provide assistance, while the tutee agent provides collaboration assistance by using the hind tool. We presented our idea on a prototype game called “Pyramid Programming Game,” a 2D game that was developed using Libgdx. The game's Pyramid component symbolizes a programming task that is presented to the player in the form of a puzzle. During gameplay, the Agents can instruct, direct, inspire, and communicate emotions. They can also rapidly alter the instructional pattern in response to the learner's performance and knowledge. The pyramid must be effectively destroyed in order to win the game. The game also teaches and illustrates the advantages of utilizing educational agents such as TrA and TeA to assist and motivate students. Our findings support the idea that the functionality of a pedagogical agent should be dualized into an instructional and learner’s companion agent in order to enhance interactivity in a game-based environment.

Keywords: tutor agent, tutee agent, learner’s companion interaction, agent collaboration

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Authors: Mao Zhaofang, Xu Yida, Fang Kan, Fu Enyuan, Zhao Zhao

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Nowadays, last-mile distribution plays an increasingly important role in the whole industrial chain delivery link and accounts for a large proportion of the whole distribution process cost. Promoting the upgrading of logistics networks and improving the layout of final distribution points has become one of the trends in the development of modern logistics. Due to the discrete and heterogeneous needs and spatial distribution of customer demand, which will lead to a higher delivery failure rate and lower vehicle utilization, last-mile delivery has become a time-consuming and uncertain process. As a result, courier companies have introduced a range of innovative parcel storage facilities, including pick-up points and lockers. The introduction of pick-up points and lockers has not only improved the users’ experience but has also helped logistics and courier companies achieve large-scale economy. Against the backdrop of the COVID-19 of the previous period, contactless delivery has become a new hotspot, which has also created new opportunities for the development of collection services. Therefore, a key issue for logistics companies is how to design/redesign their last-mile distribution network systems to create integrated logistics and distribution networks that consider pick-up points and lockers. This paper focuses on the introduction of self-pickup facilities in new logistics and distribution scenarios and the heterogeneous demands of customers. In this paper, we consider two types of demand, including ordinary products and refrigerated products, as well as corresponding transportation vehicles. We consider the constraints associated with self-pickup points and lockers and then address the location-routing problem with self-pickup facilities and heterogeneous demands (LRP-PFHD). To solve this challenging problem, we propose a mixed integer linear programming (MILP) model that aims to minimize the total cost, which includes the facility opening cost, the variable transport cost, and the fixed transport cost. Due to the NP-hardness of the problem, we propose a hybrid adaptive large-neighbourhood search algorithm to solve LRP-PFHD. We evaluate the effectiveness and efficiency of the proposed algorithm by using instances generated based on benchmark instances. The results demonstrate that the hybrid adaptive large neighbourhood search algorithm is more efficient than MILP solvers such as Gurobi for LRP-PFHD, especially for large-scale instances. In addition, we made a comprehensive analysis of some important parameters (e.g., facility opening cost and transportation cost) to explore their impacts on the results and suggested helpful managerial insights for courier companies.

Keywords: city logistics, last-mile delivery, location-routing, adaptive large neighborhood search

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Authors: Maria Francesca Scannone, Martina Bochicchio

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One of the major environmental problems today is hydrocarbon contamination. The promising sustainable technologies for the treatment of these contaminated sites involves the use of biological organisms. In Agri Valley (Basilicata Region) there is a living laboratory (natural oil seeps) where the selective pressure has enriched the environmental matrices with microorganisms, fungi and plant species able to use the hydrocarbons as a source of metabolic energy, to degrade or tolerate hydrocarbons. Observers visiting this area are fascinated by its unspoiled nature, and the condition of the ecosystem does not appear to has been damaged. The amazing resiliency observed in Tramutola site is of key importance to try to bring green remediation technologies, but no research has been done to identify high-performing native species. The aim of this research was to study how natural processes affect the fate of released oil or how individual species or communities of plants and animals are capable of dealing with the burden of otherwise toxic chemicals. The survey of vegetation was carried out, more than 60 species have been identified and divided into tree, shrub and herb layer. Plant data sheets have been completed only for the species that showed the most appropriate properties for phytoremediation. In general, members of the Salicales, Cyperales, Poales, Fagales, Cornales, Equisetales orders were the most commonly identified orders. They are pioneer plants with high adaptive capacity and vegetative propagation. The literature review has highlighted the existence of rhizosphere effect and a green liver model on selected plants. The study provides significant information on the environmental stress adaptation processes of many indigenous plants that are living and growing on a natural leak of crude oil and gas that migrates up through subsurface.

Keywords: green liver, hydrocarbon degradation, oil seeps, phytoremediation

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Authors: Anne Heirman, Vincent van der Noort, Rob van Son, Marije Petersen, Lisette van der Molen, Gyorgy Halmos, Richard Dirven, Michiel van den Brekel

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Objective: Voice prosthesis leakage significantly impacts laryngectomies patients' quality of life, causing insecurity and frequent unplanned hospital visits and costs. In this study, the concept of prophylactic voice prosthesis replacement was explored to prevent leakages. Study Design: A retrospective cohort study. Setting: Tertiary hospital. Methods: Device lifetimes and voice prosthesis replacements of a retrospective cohort, including all patients with laryngectomies between 2000 and 2012 in the Netherlands Cancer Institute, were used to calculate the number of needed voice prostheses per patient per year when preventing 70% of the leakages by prophylactic replacement. Various strategies for the timing of prophylactic replacement were considered: Adaptive strategies based on the individual patient’s history of replacement and fixed strategies based on the results of patients with similar voice prosthesis or treatment characteristics. Results: Patients used a median of 3.4 voice prostheses per year (range 0.1-48.1). We found a high inter-and intrapatient variability in device lifetime. When applying prophylactic replacement, this would become a median of 9.4 voice prostheses per year, which means replacement every 38 days, implying more than six additional voice prostheses per patient per year. The individual adaptive model showed that preventing 70% of the leakages was impossible for most patients, and only a median of 25% can be prevented. Monte-Carlo simulations showed that prophylactic replacement is not feasible due to the high Coefficient of Variation (Standard Deviation/Mean) in device lifetime. Conclusion: Based on our simulations, prophylactic replacement of voice prostheses is not feasible due to high inter-and intrapatient variation in device lifetime.

Keywords: voice prosthesis, voice rehabilitation, total laryngectomy, prosthetic leakage, device lifetime

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Authors: Vytautas Galvanauskas, Rimvydas Simutis, Donatas Levisauskas, Vykantas Grincas, Renaldas Urniezius

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The paper deals with model-based development and implementation of efficient control strategies for recombinant protein synthesis in fed-batch E.coli cultivation processes. Based on experimental data, a kinetic dynamic model for cultivation process was developed. This model was used to determine substrate feeding strategies during the cultivation. The proposed feeding strategy consists of two phases – biomass growth phase and recombinant protein production phase. In the first process phase, substrate-limited process is recommended when the specific growth rate of biomass is about 90-95% of its maximum value. This ensures reduction of glucose concentration in the medium, improves process repeatability, reduces the development of secondary metabolites and other unwanted by-products. The substrate limitation can be enhanced to satisfy restriction on maximum oxygen transfer rate in the bioreactor and to guarantee necessary dissolved carbon dioxide concentration in culture media. In the recombinant protein production phase, the level of substrate limitation and specific growth rate are selected within the range to enable optimal target protein synthesis rate. To account for complex process dynamics, to efficiently exploit the oxygen transfer capability of the bioreactor, and to maintain the required dissolved oxygen concentration, adaptive control algorithms for dissolved oxygen control have been proposed. The developed model-based control strategies are useful in scale-up of cultivation processes and accelerate implementation of innovative biotechnological processes for industrial applications.

Keywords: adaptive algorithms, model-based control, recombinant E. coli, scale-up of bioprocesses

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Authors: Jeng-Shin Sheu, Qi-Xun Zheng

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This paper mainly discusses a method of separating speech when using a microphone array without knowing the number and direction of sound sources. In recent years, there have been many studies on the method of separating signals by using masking, but most of the separation methods must be operated under the condition of a known number of sound sources. Such methods cannot be used for real-time applications. In our method, this paper uses Circular-Integrated-Cross-Spectrum to estimate the statistical histogram distribution of the direction of arrival (DOA) to obtain the number of sound sources and sound in the mixed-signal Source direction. In calculating the relevant parameters of the ring integrated cross-spectrum, the phase (Phase of the Cross-Power Spectrum) and phase rotation factors (Phase Rotation Factors) calculated by the cross power spectrum of each microphone pair are used. In the part of separating speech, it uses the DOA weighting and shielding separation method to calculate the sound source direction (DOA) according to each T-F unit (time-frequency point). The weight corresponding to each T-F unit can be used to strengthen the intensity of each sound source from the T-F unit and reduce the influence of the remaining sound sources, thereby achieving voice separation.

Keywords: real-time, spectrum analysis, sound source localization, sound source separation

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Authors: A. Moawad, K. C. Yao, A. Mansour, R. Gautier

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The detecting of an occupied frequency band is a major issue in cognitive radio systems. The detection process becomes difficult if the signal occupying the band of interest has faded amplitude due to multipath effects. These effects make it hard for an occupying user to be detected. This work mitigates the missed-detection problem in the context of cognitive radio in frequency-selective fading channel by proposing blind channel estimation method that is based on scattering transform. By initially applying conventional energy detection, the missed-detection probability is evaluated, and if it is greater than or equal to 50%, channel estimation is applied on the received signal followed by channel equalization to reduce the channel effects. In the proposed channel estimator, we modify the Morlet wavelet by using its first derivative for better frequency resolution. A mathematical description of the modified function and its frequency resolution is formulated in this work. The improved frequency resolution is required to follow the spectral variation of the channel. The channel estimation error is evaluated in the mean-square sense for different channel settings, and energy detection is applied to the equalized received signal. The simulation results show improvement in reducing the missed-detection probability as compared to the detection based on principal component analysis. This improvement is achieved at the expense of increased estimator complexity, which depends on the number of wavelet filters as related to the channel taps. Also, the detection performance shows an improvement in detection probability for low signal-to-noise scenarios over principal component analysis- based energy detection.

Keywords: channel estimation, cognitive radio, scattering transform, spectrum sensing

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Authors: Funmilayo Sarah Eguakun, Peter Oluremi Adesoye

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One of the major problems being faced by human society is that the global temperature is believed to be rising due to human activity that releases carbon IV oxide (CO2) to the atmosphere. Carbon IV oxide is the most important greenhouse gas influencing global warming and possible climate change. With climate change becoming alarming, reducing CO2 in our atmosphere has become a primary goal of international efforts. Forest landsare major sink and could absorb large quantities of carbon if the trees are judiciously managed. The study aims at estimating the carbon sequestration capacity of Pinus caribaea (pine)and Tectona grandis (Teak) under the prevailing environmental conditions and exploring tree growth variables that influencesthe carbon sequestration capacity in Omo Forest Reserve, Ogun State, Nigeria. Improving forest management by manipulating growth characteristics that influences carbon sequestration could be an adaptive strategy of forestry to climate change. Random sampling was used to select Temporary Sample Plots (TSPs) in the study area from where complete enumeration of growth variables was carried out within the plots. The data collected were subjected to descriptive and correlational analyses. The results showed that average carbon stored by Pine and Teak are 994.4±188.3 Kg and 1350.7±180.6 Kg respectively. The difference in carbon stored in the species is significant enough to consider choice of species relevant in climate change adaptation strategy. Tree growth variables influence the capacity of the tree to sequester carbon. Height, diameter, volume, wood density and age are positively correlated to carbon sequestration. These tree growth variables could be manipulated by the forest manager as an adaptive strategy for climate change while plantations of high wood density speciescould be relevant for management strategy to increase carbon storage.

Keywords: adaptation, carbon sequestration, climate change, growth variables, wood density

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Authors: Norman Sinnigen, Igor Izyurov, Marina Krylova, Hamidreza Jamalabadi, Sarah Alizadeh, Martin Walter

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Background: EEG is a non-invasive brain activity recording technique with a high temporal resolution that allows the use of real-time applications, such as neurofeedback. However, EEG data are susceptible to electrooculographic (EOG) and electromyography (EMG) artifacts (i.e., jaw clenching, teeth squeezing and forehead movements). Due to their non-stationary nature, these artifacts greatly obscure the information and power spectrum of EEG signals. Many EEG artifact correction methods are too time-consuming when applied to low-density EEG and have been focusing on offline processing or handling one single type of EEG artifact. A software-only real-time method for correcting multiple types of EEG artifacts of high-density EEG remains a significant challenge. Methods: We demonstrate an improved approach for automatic real-time EEG artifact correction of EOG and EMG artifacts. The method was tested on three healthy subjects using 64 EEG channels (Brain Products GmbH) and a sampling rate of 1,000 Hz. Captured EEG signals were imported in MATLAB with the lab streaming layer interface allowing buffering of EEG data. EMG artifacts were detected by channel variance and adaptive thresholding and corrected by using channel interpolation. Real-time independent component analysis (ICA) was applied for correcting EOG artifacts. Results: Our results demonstrate that the algorithm effectively reduces EMG artifacts, such as jaw clenching, teeth squeezing and forehead movements, and EOG artifacts (horizontal and vertical eye movements) of high-density EEG while preserving brain neuronal activity information. The average computation time of EOG and EMG artifact correction for 80 s (80,000 data points) 64-channel data is 300 – 700 ms depending on the convergence of ICA and the type and intensity of the artifact. Conclusion: An automatic EEG artifact correction algorithm based on channel variance, adaptive thresholding, and ICA improves high-density EEG recordings contaminated with EOG and EMG artifacts in real-time.

Keywords: EEG, muscle artifacts, ocular artifacts, real-time artifact correction, real-time ICA

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Authors: Georges Ghazi, Magali Gelhaye, Ruxandra Botez

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Over the years, the Flight Management System (FMS) has experienced a continuous improvement of its many features, to the point of becoming the pilot’s primary interface for flight planning operation on the airplane. With the assistance of the FMS, the concept of distance and time has been completely revolutionized, providing the crew members with the determination of the optimized route (or flight plan) from the departure airport to the arrival airport. To accomplish this function, the FMS needs an accurate Aircraft Performance Model (APM) of the aircraft. In general, APMs that equipped most modern FMSs are established before the entry into service of an individual aircraft, and results from the combination of a set of ordinary differential equations and a set of performance databases. Unfortunately, an aircraft in service is constantly exposed to dynamic loads that degrade its flight characteristics. These degradations endow two main origins: airframe deterioration (control surfaces rigging, seals missing or damaged, etc.) and engine performance degradation (fuel consumption increase for a given thrust). Thus, after several years of service, the performance databases and the APM associated to a specific aircraft are no longer representative enough of the actual aircraft performance. It is important to monitor the trend of the performance deterioration and correct the uncertainties of the aircraft model in order to improve the accuracy the flight management system predictions. The basis of this research lies in the new ability to continuously update an Aircraft Performance Model (APM) during flight using an adaptive lookup table technique. This methodology was developed and applied to the well-known Cessna Citation X business aircraft. For the purpose of this study, a level D Research Aircraft Flight Simulator (RAFS) was used as a test aircraft. According to Federal Aviation Administration the level D is the highest certification level for the flight dynamics modeling. Basically, using data available in the Flight Crew Operating Manual (FCOM), a first APM describing the variation of the engine fan speed and aircraft fuel flow w.r.t flight conditions was derived. This model was next improved using the proposed methodology. To do that, several cruise flights were performed using the RAFS. An algorithm was developed to frequently sample the aircraft sensors measurements during the flight and compare the model prediction with the actual measurements. Based on these comparisons, a correction was performed on the actual APM in order to minimize the error between the predicted data and the measured data. In this way, as the aircraft flies, the APM will be continuously enhanced, making the FMS more and more precise and the prediction of trajectories more realistic and more reliable. The results obtained are very encouraging. Indeed, using the tables initialized with the FCOM data, only a few iterations were needed to reduce the fuel flow prediction error from an average relative error of 12% to 0.3%. Similarly, the FCOM prediction regarding the engine fan speed was reduced from a maximum error deviation of 5.0% to 0.2% after only ten flights.

Keywords: aircraft performance, cruise, trajectory optimization, adaptive lookup tables, Cessna Citation X

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Authors: Su-Hyun Jung, Chang-Bin Ha, Hyoung-Kyu Song

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Recently, increasing the quality of experience (QoE) is an important issue. Since performance degradation at cell edge extremely reduces the QoE, several techniques are defined at LTE/LTE-A standard to remove inter-cell interference (ICI). However, the conventional techniques have disadvantage because there is a trade-off between resource allocation and reliable communication. The proposed scheme reduces the ICI more efficiently by using channel state information (CSI) smartly. It is shown that the proposed scheme can reduce the ICI with less resources.

Keywords: adaptive beamforming, CoMP, LTE-A, ICI reduction

Procedia PDF Downloads 468

Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

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Background modeling and subtraction in video analysis has been widely proved to be an effective method for moving objects detection in many computer vision applications. Over the past years, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are two of the most frequently occurring issues in the practical situation. This paper presents a new two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean values of RGB color channels. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block-wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the outputs of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate a very competitive performance compared to previous models.

Keywords: background subtraction, codebook model, local binary pattern, dynamic background, illumination change

Procedia PDF Downloads 217

Authors: Yong-Kian Lim, Khan Cheung, Xin Dang, Steven Roberts, Xiaotong Wang, Vengatesen Thiyagarajan

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Unprecedented rate of increased CO₂ level in the ocean and the subsequent changes in carbonate system including decreased pH, known as ocean acidification (OA), is predicted to disrupt not only the calcification process but also several other physiological and developmental processes in a variety of marine organisms, including edible oysters. Nonetheless, not all species are vulnerable to those OA threats, e.g., some species may be able to cope with OA stress using environmentally induced modifications on gene and protein expressions. For example, external environmental stressors, including OA, can influence the addition and removal of methyl groups through epigenetic modification (e.g., DNA methylation) process to turn gene expression “on or off” as part of a rapid adaptive mechanism to cope with OA. In this study, the above hypothesis was tested through testing the effect of OA, using decreased pH 7.4 as a proxy, on the DNA methylation pattern of an endemic and a commercially important estuary oyster species, Crassostrea hongkongensis, at the time of larval habitat selection and metamorphosis. Larval growth rate did not differ between control pH 8.1 and treatment pH 7.4. The metamorphosis rate of the pediveliger larvae was higher at pH 7.4 than those in control pH 8.1; however, over one-third of the larvae raised at pH 7.4 failed to attach to an optimal substrate as defined by biofilm presence. During larval development, a total of 130 genes were differentially methylated across the two treatments. The differential methylation in the larval genes may have partially accounted for the higher metamorphosis success rate under decreased pH 7.4 but with poor substratum selection ability. Differentially methylated loci were concentrated in the exon regions and appear to be associated with cytoskeletal and signal transduction, oxidative stress, metabolic processes, and larval metamorphosis, which implies the high potential of C. hongkongensis larvae to acclimate and adapt through non-genetic ways to OA threats within a single generation.

Keywords: adaptive plasticity, DNA methylation, larval metamorphosis, ocean acidification

Procedia PDF Downloads 139

Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

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Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

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Authors: Pawel Flaszynski, Piotr Doerffer, Jan Holnicki-Szulc, Grzegorz Mikulowski

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Results of numerical simulations for transonic flow in a piezoelectric valve are presented. The valve is the main part of an adaptive pneumatic shock absorber. Flow structure in the valve domain and the influence of the flow non-uniformity in the valve on a mass flow rate is investigated. Numerical simulation results are compared with experimental data.

Keywords: pneumatic valve, transonic flow, numerical simulations, piezoelectric valve

Procedia PDF Downloads 514

Authors: Prisa Damrongsiri, Kittinan Pongpianskul, Mario Kubek, Herwig Unger

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Apps are today the most important possibility to adapt mobile phones and computers to fulfill the special needs of their users. Location- and context-sensitive programs are hereby the key to support the interaction of the user with his/her environment and also to avoid an overload with a plenty of dispensable information. The contribution shows, how a trusted, secure and really bi-directional communication and interaction among users and their environment can be established and used, e.g. in the field of home automation.

Keywords: apps, context-sensitive, location-sensitive, self-configuration, mobile computing, smart home

Procedia PDF Downloads 396