Search results for: Rule Based Architecture

Authors: Rachelly Ashwall, Ephraim Tabory

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In an attempt to reduce the level of a major identity-based conflict in Israel between Ultra-orthodox and secular Jews, several initiatives in recent years have tried to bring members of the two societies together in facilitated joint discussion forums. Our study analyzes the impact of two types of such programs: joint mediation training classes and confrontation-based learning programs that are designed to facilitate discussions over controversial issues. These issues include claims about an unequal shouldering of national obligations such as military service, laws requiring public observance of the Sabbath, and discrimination against women, among others. The study examines the factors that enabled the two groups to reduce their social distance, and increase their understanding of each other, and develop a recognition and tolerance of the other group's particular social identity. The research conducted over a course of two years involved observations of the activities of the groups, interviews with the participants, and analysis of the social media used by the groups. The findings demonstrate the progression from a mutual initial lack of knowledge about habits, norms, and attitudes of the out-group to an increasing desire to know, understand and more readily accept the identity of a previously rejected outsider. Participants manifested more respect, concern for and even affection for those whose identity initially led them to reject them out of hand. We discuss the implications for seemingly intractable identity-based conflict in fragile societies.

Keywords: identity-based conflict, intergroup relations, joint mediation learning, out-group recognition, social identity

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Authors: Normah Sulaiman, Yong Zhi Kang, Nor Hayati Hussain, Abdul Rehman Khalid

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Segara Ninda owned by Din Ku Meh, the governor of the province Satul, a Malay man with a big role liaising with Thailand. This mansion is part of the legacy he left behind among other properties in George Town, Penang, besides his family. The island’s geographical location is strategic which has benefitted it through important trade routes for Europe, Middle, East, India, and China in the past. Due to this reasoning, various architectural styles were introduced in Penang; Late Straits Eclectic style is one of the forms of the Colonial Architectural style widely spread as vernacular shophouses in George Town. Segara Ninda is located among the mixture of nouveau-riche, historical and heritage sites at the most important street; Penang Road, which dated back to the late 18th century. This paper examines the strait eclectic style that Segara Ninda encompasses. Acknowledging the mixture of colonial architecture in Georgetown, we argue that the mansion faces challenging issues in conservation processes to be vindicated. This is reflected by analysing the spatial layout, visual elements quality, and its activity through interviews with the occupants of the mansion. The focus will be on the understanding of building form, features, and functions; respecting the architectural spaces and their activity. The methodology applied is to promote our understanding of the mix of culture that the mansion holds through documentation, observation and measuring exercises. This offers a positional interpretation of the mix of culture that the mansion holds. This conservation effort will further contribute exposure to the public and recognize it in the society as its essence is a deficiency character to the existing built environment.

Keywords: eclectic, heritage, spatial organization, culture

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Authors: Ali A. Karakhan

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Stakeholders in the architecture, engineering, and construction (AEC) industry have been always searching for strategies to develop, design, and construct healthy and smart building projects. Healthy and smart building projects require that the building process including design and construction be altered and carefully implemented in order to bring about sustainable outcomes throughout the facility lifecycle. Healthy and smart building projects are expected to positively influence organizational success and facility performance across the project lifecycle leading to superior outcomes in terms of people, economy, and the environment. The present study aims to identify potential strategies that AEC organizations can implement to achieve healthy and smart building projects. Drivers and barriers for healthy and smart building features are also examined. The study findings indicate that there are three strategies to advance the development of healthy and smart building projects: (1) the incorporation of high-quality products and low chemical-emitting materials, (2) the integration of innovative designs, methods, and practices, and (3) the adoption of smart technology throughout the facility lifecycle. Satisfying external demands, achievement of a third-party certification, obtaining financial incentives, and a desire to fulfill professional duty are identified as the key drivers for developing healthy and smart building features; whereas, lack of knowledge and training, time/cost constrains, preference for/adherence to customary practices, and unclear business case for why healthy buildings are advantageous are recognized as the primary barriers toward a wider diffusion of healthy and smart building projects. The present study grounded in previous engineering, medical, and public health research provides valuable technical and practical recommendations for facility owners and industry professionals interested in pursuing sustainable, yet healthy and smart building projects.

Keywords: healthy buildings, smart construction, innovative designs, sustainable projects

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Authors: Mehdi Shahbazian, Alireza Aarabi, Mohsen Hadiyan

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Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot.

Keywords: evolutionary algorithm, genetic algorithm, particle swarm optimization, PID controller

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Rahim Jafari, Tuba Okutucu-Özyurt

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The hydrodynamics and heat transfer characteristics of a vaporized elongated bubble in a rectangular microchannel have been simulated based on Cahn-Hilliard phase-field method. In the simulations, the initially nucleated bubble starts growing as it comes in contact with superheated water. The growing shape of the bubble compared with the available experimental data in the literature.

Keywords: microchannel, boiling, Cahn-Hilliard method, simulation

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Authors: Mei-Fang Wu, Shu-Li Wang, Tsung-Yueh Lu, Feng-Tsung Cheng

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Strengthening core competitiveness is the main goal of enterprises in a fierce competitive environment. Accurate cost information is a great help for managers in dealing with operation strategies. This paper establishes a profitability management mechanism that applies the Activity-Based Benefit approach (ABBA) to solve the profitability for each customer from the market. ABBA provides financial and non-financial information for the operation, but also indicates what resources have expired in the operational process. The customer profit management model shows the level of profitability of each customer for the company. The empirical data were gathered from a case company operating in the leather industry in Taiwan. The research findings indicate that 30% of customers create little profit for the company as a result of asking for over 5% of sales discounts. Those customers ask for sales discount because of color differences of leather products. This paper provides a customer’s profitability evaluation mechanism to help enterprises to greatly improve operating effectiveness and promote operational activity efficiency and overall operation profitability.

Keywords: activity-based benefit approach, customer profit analysis, leather industry, profitability management mechanism

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Authors: Amir Zand-Moghadam, Mahnaz Alizadeh

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The aim of the present study was to investigate the effect of teacher-scaffolding versus peer-scaffolding on EFL learners’ metapragmatic awareness in the paradigm of task-based language teaching (TBLT). To this end, a number of dialogic information-gap tasks requiring two-way interactant relationship were designed for the five speech acts of request, refusal, apology, suggestion, and compliment following Ellis’s (2003) model. Then, 48 intermediate EFL learners were randomly selected, homogenized, and assigned to two groups: 26 participants in the teacher-scaffolding group (Group One) and 22 in the peer-scaffolding group (Group Two). While going through the three phases of pre-task, while-task, and post-task, the participants in the first group completed the designed tasks by the teacher’s interaction, scaffolding, and feedback. On the other hand, the participants in the second group were required to complete the tasks in expert-novice pairs through peer scaffolding in all the three phases of a task-based syllabus. The findings revealed that the participants in the teacher-scaffolding group developed their L2 metapragmatic awareness more than the peer-scaffolding group. Thus, it can be concluded that teacher-scaffolding is more effective than peer scaffolding in developing metapragmatic awareness among EFL learners. It can also be claimed that the use of tasks can be more influential when they are accompanied by teacher-scaffolding. The findings of the present study have implications for language teachers and researchers.

Keywords: ILP, metapragmatic awareness, scaffolding, task-based instruction

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Authors: Yang Yu, Jian Wang, Jiqiang Liu, Lei Han, Xudong He, Shaohua Lv

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As the increasing network attacks become more and more complex, network situation assessment based on log analysis cannot meet the requirements to ensure network security because of the low quality of logs and alerts. This paper addresses the lack of consideration of security attributes of hosts and attacks in the network. Identity and effectiveness of Distributed Denial of Service (DDoS) are hard to be proved in risk assessment based on alerts and flow matching. This paper proposes a multi-dimension threat situation assessment method based on network security attributes. First, the paper offers an improved Common Vulnerability Scoring System (CVSS) calculation, which includes confident risk, integrity risk, availability risk and a weighted risk. Second, the paper introduces deterioration rate of properties collected by sensors in hosts and network, which aimed at assessing the time and level of DDoS attacks. Third, the paper introduces distribution of asset value in security attributes considering features of attacks and network, which aimed at assessing and show the whole situation. Experiments demonstrate that the approach reflects effectiveness and level of DDoS attacks, and the result can show the primary threat in network and security requirement of network. Through comparison and analysis, the method reflects more in security requirement and security risk situation than traditional methods based on alert and flow analyzing.

Keywords: DDoS evaluation, improved CVSS, network security attribute, threat situation assessment

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Authors: Hafiz Awais Ahmad

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This paper aims to examine the root causes of gender-based violence (GBV) in Pakistan and proposes strategies to address this issue. Using a qualitative approach, this study analyzed data from various sources, including interviews with survivors of GBV and experts in the field. The findings revealed that GBV in Pakistan is deeply rooted in patriarchal attitudes and practices, economic insecurity, lack of education, and limited access to justice. The study recommends a multi-faceted approach to address GBV, including legislative reforms, awareness-raising campaigns, economic empowerment, and improved access to justice for survivors. Furthermore, the study highlights the importance of engaging men and boys in efforts to address GBV and promote gender equality. The findings of this study have important implications for policy-makers, practitioners, and researchers working towards ending GBV in Pakistan.

Keywords: gender-based violence, Pakistan, legislative reforms, advocacy

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Authors: Behrooz Movahedi

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Fe-based amorphous feedstock powders are used as the matrix into which various ratios of hard B4C nanoparticles (0, 5, 10, 15, 20 vol.%) as reinforcing agents were prepared using a planetary high-energy mechanical milling. The ball-milled nanocomposite feedstock powders were also sprayed by means of high-velocity oxygen fuel (HVOF) technique. The characteristics of the powder particles and the prepared coating depending on their microstructures and nanohardness were examined in detail using nanoindentation tester. The results showed that the formation of the Fe-based amorphous phase was noticed over the course of high-energy ball milling. It is interesting to note that the nanocomposite coating is divided into two regions, namely, a full amorphous phase region and homogeneous dispersion of B4C nanoparticles with a scale of 10–50 nm in a residual amorphous matrix. As the B4C content increases, the nanohardness of the composite coatings increases, but the fracture toughness begins to decrease at the B4C content higher than 20 vol.%. The optimal mechanical properties are obtained with 15 vol.% B4C due to the suitable content and uniform distribution of nanoparticles. Consequently, the changes in mechanical properties of the coatings were attributed to the changes in the brittle to ductile transition by adding B4C nanoparticles.

Keywords: Fe-based amorphous, B₄C nanoparticles, nanocomposite coating, HVOF

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Authors: Burak Yigit Katanalp, Murat Tastan, Perviz Ahmedzade, çIgdem Canbay Turkyilmaz, Emrah Turkyilmaz

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This study evaluated the mechanical and rheological performance of fly ash-based geopolymer at high temperatures. A series of laboratory tests were conducted on neat bitumen and three modified bitumen samples, which incorporated fly ash-based geopolymer at various percentages. Low-calcium fly ash was used as the alumina-silica source. The dynamic shear rheometer and rotational viscometer were employed to determine high-temperature properties, while conventional tests such as penetration and softening point were used to evaluate the physical properties of bitumen. The short-term aging resistance of the samples was assessed using the rolling thin film oven. The results show that geopolymer has a compromising effect on bitumen properties, with improved stiffness, enhanced mechanical strength, and increased thermal susceptibility of the asphalt binder.

Keywords: bitumen, geopolymer, modification, dynamic mechanical analysis

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Authors: Amir Ali Fatoorchi

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Regardless of the advantage of LoT devices, they have limitations like storage, compute, and security problems. In recent years, a lot of Blockchain-based research in IoT published and presented. In this paper, we present the Security issues of LoT. IoT has three levels of security issues: Low-level, Intermediate-level, and High-level. We survey and compare blockchain-based solutions for high-level security issues and show how the underlying technology of bitcoin and Ethereum could solve IoT problems.

Keywords: Blockchain, security, data security, IoT

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Authors: Ramy Kamel Fekrey Gadelrab

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Textile sensors have gained a lot of interest in recent years as it is instrumental in monitoring physiological and environmental changes, for a better diagnosis that can be useful in various fields like medical textiles, sports textiles, protective textiles, agro textiles, and geo-textiles. Moreover, with the development of flexible textile-based wearable sensors, the functionality of smart clothing is augmented for a more improved user experience when it comes to technical textiles. In this context, conductive textiles using new composites and nanomaterials are being developed while considering its compatibility with the textile manufacturing processes. This review aims to provide a comprehensive and detailed overview of the contemporary advancements in textile-based wearable physical sensors, used in the field of medical, security, surveillance, and protection, from a global perspective. The methodology used is through analysing various examples of integration of wearable textile-based sensors with clothing for daily use, keeping in mind the technological advances in the same. By comparing various case studies, it come across various challenges textile sensors, in terms of stability, the comfort of movement, and reliable sensing components to enable accurate measurements, in spite of progress in the engineering of the wearable. Addressing such concerns is critical for the future success of wearable sensors.

Keywords: nanoparticles, enzymes, immobilization, textilesconductive yarn, e-textiles, smart textiles, thermal analysisflexible textile-based wearable sensors, contemporary advancements, conductive textiles, body conformal design

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Authors: Jinsiang Shaw, Sheng-Xiang Xu

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This paper presents the development of an autonomous automated guided vehicle (AGV) with a robot arm attached on top of it within the framework of robot operation system (ROS). ROS can provide libraries and tools, including hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, etc. For this reason, this AGV can provide automatic navigation and parts transportation and pick-and-place task using robot arm for typical industrial production line use. More specifically, this AGV will be controlled by an on-board host computer running ROS software. Command signals for vehicle and robot arm control and measurement signals from various sensors are transferred to respective microcontrollers. Users can operate the AGV remotely through the TCP / IP protocol and perform SLAM (Simultaneous Localization and Mapping). An RGBD camera and LIDAR sensors are installed on the AGV, using these data to perceive the environment. For SLAM, Gmapping is used to construct the environment map by Rao-Blackwellized particle filter; and AMCL method (Adaptive Monte Carlo localization) is employed for mobile robot localization. In addition, current AGV position and orientation can be visualized by ROS toolkit. As for robot navigation and obstacle avoidance, A* for global path planning and dynamic window approach for local planning are implemented. The developed ROS AGV with a robot arm on it has been experimented in the university factory. A 2-D and 3-D map of the factory were successfully constructed by the SLAM method. Base on this map, robot navigation through the factory with and without dynamic obstacles are shown to perform well. Finally, pick-and-place of parts using robot arm and ensuing delivery in the factory by the mobile robot are also accomplished.

Keywords: automated guided vehicle, navigation, robot operation system, Simultaneous Localization and Mapping

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Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Xue Xiao, Sun Hao, Liu Donghua

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E-commerce is experiencing rapid development and evolution, but traditional research methods are difficult to fully demonstrate the relationship between micro factors and macro evolution in the development process of e-commerce, which cannot provide accurate assessment for the existing strategies and predict the future evolution trends. To solve these problems, this paper presents the concept of e-commerce service ecosystem based on the characteristics of e-commerce and business ecosystem theory, describes e-commerce environment as a complex adaptive system from the perspective of ecology, constructs a e-commerce service ecosystem model by using Agent-based modeling method and Java language in RePast simulation platform and conduct experiment through the way of computational experiment, attempt to provide a suitable and effective researching method for the research on e-commerce evolution. By two experiments, it can be found that system model built in this paper is able to show the evolution process of e-commerce service ecosystem and the relationship between micro factors and macro emergence. Therefore, the system model constructed by Agent-based method and computational experiment provides proper means to study the evolution of e-commerce ecosystem.

Keywords: e-commerce service ecosystem, complex system, agent-based modeling, computational experiment

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Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

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Authors: Ankur Jain, W. Wilfred Godfrey

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Embedded systems are usually microcontroller-based systems that represent a class of reliable and dependable dedicated computer systems designed for specific purposes. Micro-controllers are used in most electronic devices in an endless variety of ways. Some micro-controller-based embedded systems are required to respond to external events in the shortest possible time and such systems are known as real-time embedded systems. So in multitasking system there is a need of task Scheduling,there are various scheduling algorithms like Fixed priority Scheduling(FPS),Earliest deadline first(EDF), Rate Monotonic(RM), Deadline Monotonic(DM),etc have been researched. In this Report various conventional algorithms have been reviewed and analyzed, these algorithms consists of single shape task, A new Multishape scheduling algorithms has been proposed and implemented and analyzed.

Keywords: dm, edf, embedded systems, fixed priority, microcontroller, rtos, rm, scheduling algorithms

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Authors: H. Benaldjia, N. Habib, F. Djefaflia, A. Nait-Merzoug, A. Harat, J. El-Haskouri, O. Guellati

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Currently, the technology has attracted knowledge of energy storage sources similar to batteries, capacitors and super-capacitors because of its very different applications in many fields with major social and economic challenges. Moreover, hydroxides have attracted much attention as a promising and active material choice in large-scale applications such as molecular adsorption/storage and separation for the environment, ion exchange, nanotechnology, supercapacitor for energy storage and conversion, electro-biosensing, and catalysts, due to their unique properties which are strongly influenced by their composition, microstructure, and synthesis method. In this context, we report in this study the synthesis of hydroxide-based nanomaterials precisely based on Ni and Fe using a simple hydrothermal method with mono and bi precursors at optimized growth conditions (6h-120°C). The obtained products were characterized using different techniques, such as XRD, FTIR, FESEM and BET, as well as electrochemical measurements.

Keywords: energy storage, Supercapacitors, nanocomposites, nanohybride, electro-active materials.

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Authors: Kamal Ray, Tapati Bhattacharya

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Private use of public office for private gain could be a tentative definition of corruption and most distasteful event of corruption is that it is not there, nor that it is pervasive, but it is socially acknowledged in the global economy, especially in the developing nations. We attempted to assess the interrelationship between the Corruption perception index (CPI) and the principal components of governance indicators as per World Bank like Control of Corruption (CC), rule of law (RL), regulatory quality (RQ) and government effectiveness (GE). Our empirical investigation concentrates upon the degree of reflection of governance indicators upon the CPI in order to single out the most powerful corruption-generating indicator in the selected countries. We have collected time series data on above governance indicators such as CC, RL, RQ and GE of the selected eleven countries from the year of 1996 to 2012 from World Bank data set. The countries are USA, UK, France, Germany, Greece, China, India, Japan, Thailand, Brazil, and South Africa. Corruption Perception Index (CPI) of the countries mentioned above for the period of 1996 to 2012is also collected. Graphical method of simple line diagram against the time series data on CPI is applied for quick view for the relative positions of different trend lines of different nations. The correlation coefficient is enough to assess primarily the degree and direction of association between the variables as we get the numerical data on governance indicators of the selected countries. The tool of Granger Causality Test (1969) is taken into account for investigating causal relationships between the variables, cause and effect to speak of. We do not need to verify stationary test as length of time series is short. Linear regression is taken as a tool for quantification of a change in explained variables due to change in explanatory variable in respect of governance vis a vis corruption. A bilateral positive causal link between CPI and CC is noticed in UK, index-value of CC increases by 1.59 units as CPI increases by one unit and CPI rises by 0.39 units as CC rises by one unit, and hence it has a multiplier effect so far as reduction in corruption is concerned in UK. GE causes strongly to the reduction of corruption in UK. In France, RQ is observed to be a most powerful indicator in reducing corruption whereas it is second most powerful indicator after GE in reducing of corruption in Japan. Governance-indicator like GE plays an important role to push down the corruption in Japan. In China and India, GE is proactive as well as influencing indicator to curb corruption. The inverse relationship between RL and CPI in Thailand indicates that ongoing machineries related to RL is not complementary to the reduction of corruption. The state machineries of CC in S. Africa are highly relevant to reduce the volume of corruption. In Greece, the variations of CPI positively influence the variations of CC and the indicator like GE is effective in controlling corruption as reflected by CPI. All the governance-indicators selected so far have failed to arrest their state level corruptions in USA, Germany and Brazil.

Keywords: corruption perception index, governance indicators, granger causality test, regression

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Sangeeta Palekar, Mahima Rana, Jayu Kalambe

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

Keywords: colorimetric, fluid contaminants, nitrite detection, microfluidics

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Authors: Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li

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This paper develops a robust deadlock control technique for shared and unreliable resources in automated manufacturing systems (AMSs) based on structural analysis and colored Petri nets, which consists of three steps. The first step involves using strict minimal siphon control to create a live (deadlock-free) system that does not consider resource failure. The second step uses an approach based on colored Petri net, in which all monitors designed in the first step are merged into a single monitor. The third step addresses the deadlock control problems caused by resource failures. For all resource failures in the Petri net model a common recovery subnet based on colored petri net is proposed. The common recovery subnet is added to the obtained system at the second step to make the system reliable. The proposed approach is evaluated using an AMS from the literature. The results show that the proposed approach can be applied to an unreliable complex Petri net model, has a simpler structure and less computational complexity, and can obtain one common recovery subnet to model all resource failures.

Keywords: automated manufacturing system, colored Petri net, deadlocks, siphon

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Authors: Hirokatsu Kawashima

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One important element of teaching/learning listening is intensive listening such as listening for precise sounds, words, grammatical, and semantic units. Several classroom-based investigations have been conducted to explore the usefulness of auditory recognition of phonological, grammatical and semantic errors in such a context. The current study reports the results of one such investigation, which targeted auditory recognition of phonological, grammatical, and semantic errors at the sentence level. 56 Japanese EFL learners participated in this investigation, in which their recognition performance of phonological, grammatical and semantic errors was measured on a 9-point scale by learners’ self-evaluation from the perspective of 1) two types of similar English sound (vowel and consonant minimal pair words), 2) two types of sentence word order (verb phrase-based and noun phrase-based word orders), and 3) two types of semantic consistency (verb-purpose and verb-place agreements), respectively, and their general listening proficiency was examined using standardized tests. A number of findings have been made about the interactive relationships between the three types of auditory error recognition and general listening proficiency. Analyses based on the OPLS (Orthogonal Projections to Latent Structure) regression model have disclosed, for example, that the three types of auditory error recognition are linked in a non-linear way: the highest explanatory power for general listening proficiency may be attained when quadratic interactions between auditory recognition of errors related to vowel minimal pair words and that of errors related to noun phrase-based word order are embraced (R2=.33, p=.01).

Keywords: auditory error recognition, intensive listening, interaction, investigation

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Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

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Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

Abstract:

Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Zuraidasahana Zulkarnain, Mohd Shafry Mohd Rahim, Nor Anita Fairos Ismail, Mohd Azhar M. Arsad

Abstract:

Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.

Keywords: biometrics, euclidean classifier, features extraction, offline signature verification, voting-based classifier

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Authors: Mahmut Yildirim

Abstract:

This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Jesus Rodriguez Viejo

Abstract:

The aim of this paper is to study cross-cultural interactions between the West and Byzantium, in the fields of art and religion, by analyzing the decoration of one luxury manuscript. The Spanish National Library is home to one of the most extraordinary examples of illuminated manuscript production of Norman Sicily – the Messina Missal. Dating from the late twelfth century, this liturgical book was the result of the intense activity of artistic patronage of an Englishman, Richard Palmer. Appointed bishop of the Sicilian city in the second half of the century, Palmer set a painting workshop attached to his cathedral. The illuminated manuscripts produced there combine a clear Byzantine iconographic language with a myriad of elements imported from France, such as a large number of decorated initials. The most remarkable depiction contained in the Missal is that of the Theotokos (fol. 80r). Its appearance immediately recalls portative Byzantine icons of the Mother of God in South Italy and Byzantium and implies the intervention of an artist familiar with icon painting. The richness of this image is a clear proof of the prestige that Byzantine art enjoyed in the island after the Norman takeover. The production of the school of Messina under Richard Palmer could be considered a counterpart in the field of manuscript illumination of the court art of the Sicilian kings in Palermo and the impressive commissions for the cathedrals of Monreale and Cefalù. However, the ethnic composition of Palmer’s workshop has never been analyzed and therefore, we intend to shed light on the permanent presence of Greek-speaking artists in Norman Messina. The east of the island was the last stronghold of the Greeks and soon after the Norman conquest, the previous exchanges between the cities of this territory and Byzantium restarted again, mainly by way of trade. Palmer was not a Norman statesman, but a churchman and his love for religion and culture prevailed over the wars and struggles for power of the Sicilian kingdom in the central Mediterranean. On the other hand, the representation of the Theotokos can prove that Eastern devotional approaches to images were still common in the east of the island more than a century after the collapse of Byzantine rule. Local Norman lords repeatedly founded churches devoted to Greek saints and medieval Greek-speaking authors were widely copied in Sicilian scriptoria. The Madrid Missal and its Theotokos are doubtless the product of Western initiative but in a land culturally dominated by Byzantium. Westerners, such as Palmer and his circle, could have been immersed in this Hellenophile culture and therefore, naturally predisposed to perform prayers and rituals, in both public and private contexts, linked to ideas and practices of Greek origin, such as the concept of icon.

Keywords: history of art, byzantine art, manuscripts, norman sicily, messina, patronage, devotion, iconography

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