Search results for: Volatile memory

Authors: M. Bohlouli, M. Analoui

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For complete support of Quality of Service, it is better that environment itself predicts resource requirements of a job by using special methods in the Grid computing. The exact and correct prediction causes exact matching of required resources with available resources. After the execution of each job, the used resources will be saved in the active database named "History". At first some of the attributes will be exploit from the main job and according to a defined similarity algorithm the most similar executed job will be exploited from "History" using statistic terms such as linear regression or average, resource requirements will be predicted. The new idea in this research is based on active database and centralized history maintenance. Implementation and testing of the proposed architecture results in accuracy percentage of 96.68% to predict CPU usage of jobs and 91.29% of memory usage and 89.80% of the band width usage.

Keywords: Active Database, Grid Computing, ResourceRequirement Prediction, Scheduling,

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Authors: M. Graus, K. Westhoff, X. Xu

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The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: Data analytics, green production, industrial energy management, optimization, renewable energies, simulation.

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Authors: H. Loukil, A. Ben Atitallah, F. Ghozzi, M. A. Ben Ayed, N. Masmoudi

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In MPEG and H.26x standards, to eliminate the temporal redundancy we use motion estimation. Given that the motion estimation stage is very complex in terms of computational effort, a hardware implementation on a re-configurable circuit is crucial for the requirements of different real time multimedia applications. In this paper, we present hardware architecture for motion estimation based on "Full Search Block Matching" (FSBM) algorithm. This architecture presents minimum latency, maximum throughput, full utilization of hardware resources such as embedded memory blocks, and combining both pipelining and parallel processing techniques. Our design is described in VHDL language, verified by simulation and implemented in a Stratix II EP2S130F1020C4 FPGA circuit. The experiment result show that the optimum operating clock frequency of the proposed design is 89MHz which achieves 160M pixels/sec.

Keywords: SAD, FSBM, Hardware Implementation, FPGA.

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Authors: Pejman Hosseiniun, Rose Shayeghi, Iman Rahbari, Mohamad Reza Kalhor

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CNFET has emerged as an alternative material to silicon for high performance, high stability and low power SRAM design in recent years. SRAM functions as cache memory in computers and many portable devices. In this paper, a new SRAM cell design based on CNFET technology is proposed. The proposed SRAM cell design for CNFET is compared with SRAM cell designs implemented with the conventional CMOS and FinFET in terms of speed, power consumption, stability, and leakage current. The HSPICE simulation and analysis show that the dynamic power consumption of the proposed 8T CNFET SRAM cell’s is reduced about 48% and the SNM is widened up to 56% compared to the conventional CMOS SRAM structure at the expense of 2% leakage power and 3% write delay increase.

Keywords: SRAM cell, CNFET, low power, HSPICE.

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Authors: Sven Rebhan, Nils Einecke, Julian Eggert

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In this paper we propose a method for vision systems to consistently represent functional dependencies between different visual routines along with relational short- and long-term knowledge about the world. Here the visual routines are bound to visual properties of objects stored in the memory of the system. Furthermore, the functional dependencies between the visual routines are seen as a graph also belonging to the object-s structure. This graph is parsed in the course of acquiring a visual property of an object to automatically resolve the dependencies of the bound visual routines. Using this representation, the system is able to dynamically rearrange the processing order while keeping its functionality. Additionally, the system is able to estimate the overall computational costs of a certain action. We will also show that the system can efficiently use that structure to incorporate already acquired knowledge and thus reduce the computational demand.

Keywords: Adaptive systems, Knowledge representation, Machinevision, Systems engineering

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Authors: Tatjana Eitrich, Bruno Lang

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This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machines, Shared Memory Parallel Computing, Large Data

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Authors: G. Anugkakul, S. Yordchim

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The objectives were to study language learning strategies (LLSs) employed by Chinese students, and the frequency of LLSs they used, and examine the relationship between the use of LLSs and gender. The Strategy Inventory for Language Learning (SILL) by Oxford was administered to thirty-six Chinese students at Suan Sunandha Rajabhat University in Thailand. The data obtained was analyzed using descriptive statistics and chi-square tests. Three useful findings were found on the use of LLSs reported by Chinese students. First, Chinese students used overall LLSs at a high level. Second, among the six strategy groups, Chinese students employed compensation strategy most frequently and memory strategy least frequently. Third, the research results also revealed that gender had significant effect on Chinese Student’s use of overall LLSs.

Keywords: English language, Language Learning Strategy, Chinese Students, Gender.

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Authors: Sujana Dhar, Asis Mazumdar

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The effects of global warming on India vary from the submergence of low-lying islands and coastal lands to the melting of glaciers in the Indian Himalayas, threatening the volumetric flow rate of many of the most important rivers of India and South Asia. In India, such effects are projected to impact millions of lives. As a result of ongoing climate change, the climate of India has become increasingly volatile over the past several decades; this trend is expected to continue. Climate change is one of the most important global environmental challenges, with implications for food production, water supply, health, energy, etc. Addressing climate change requires a good scientific understanding as well as coordinated action at national and global level. The climate change issue is part of the larger challenge of sustainable development. As a result, climate policies can be more effective when consistently embedded within broader strategies designed to make national and regional development paths more sustainable. The impact of climate variability and change, climate policy responses, and associated socio-economic development will affect the ability of countries to achieve sustainable development goals. A very well calibrated Soil and Water Assessment Tool (R2 = 0.9968, NSE = 0.91) was exercised over the Khatra sub basin of the Kangsabati River watershed in Bankura district of West Bengal, India, in order to evaluate projected parameters for agricultural activities. Evapotranspiration, Transmission Losses, Potential Evapotranspiration and Lateral Flow to reach are evaluated from the years 2041-2050 in order to generate a picture for sustainable development of the river basin and its inhabitants. India has a significant stake in scientific advancement as well as an international understanding to promote mitigation and adaptation. This requires improved scientific understanding, capacity building, networking and broad consultation processes. This paper is a commitment towards the planning, management and development of the water resources of the Kangsabati River by presenting detailed future scenarios of the Kangsabati river basin, Khatra sub basin, over the mentioned time period. India-s economy and societal infrastructures are finely tuned to the remarkable stability of the Indian monsoon, with the consequence that vulnerability to small changes in monsoon rainfall is very high. In 2002 the monsoon rains failed during July, causing profound loss of agricultural production with a drop of over 3% in India-s GDP. Neither the prolonged break in the monsoon nor the seasonal rainfall deficit was predicted. While the general features of monsoon variability and change are fairly well-documented, the causal mechanisms and the role of regional ecosystems in modulating the changes are still not clear. Current climate models are very poor at modelling the Asian monsoon: this is a challenging and critical region where the ocean, atmosphere, land surface and mountains all interact. The impact of climate change on regional ecosystems is likewise unknown. The potential for the monsoon to become more volatile has major implications for India itself and for economies worldwide. Knowledge of future variability of the monsoon system, particularly in the context of global climate change, is of great concern for regional water and food security. The major findings of this paper were that of all the chosen projected parameters, transmission losses, soil water content, potential evapotranspiration, evapotranspiration and lateral flow to reach, display an increasing trend over the time period of years 2041- 2050.

Keywords: Change, future water availability scenario, modeling, SWAT, global warming, sustainability.

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: Agricultural object detection, Deep learning, machine vision, YOLO family.

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Authors: Heejin Yun, Juanjuan Zang

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This study aims to analyze the text terms posted on an online community of people from the same hometown and to understand the topic and trend of nostalgia composed online. For this purpose, this study collected 144 writings which the natives of Yeongjong Island, Incheon, South-Korea have posted on an online community. And it analyzed association relations. As a result, online community texts means that just defining nostalgia as ‘a mind longing for hometown’ is not an enough explanation. Second, texts composed online have abstractness rather than persons’ individual stories. This study figured out the relationship that had the most critical and closest mutual association among the terms that constituted nostalgia through literature research and association rule concerning nostalgia. The result of this study has a characteristic that it summed up the core terms and emotions related to nostalgia.

Keywords: Nostalgia, cultural memory, data mining, online community.

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Authors: Marijana M. Ačanski, Kristian A. Pastor, Djura N. Vujić

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The identification of lipid and soluble sugar components in flour samples of different cultivars belonging to common oat species (Avena sativa L.) was performed: spring oat, winter oat and hulless oat. Fatty acids were extracted from flour samples with n-hexane, and derivatized into volatile methyl esters, using TMSH (trimethylsulfonium hydroxide in methanol). Soluble sugars were then extracted from defatted and dried samples of oat flour with 96% ethanol, and further derivatized into corresponding TMS-oximes, using hydroxylamine hydrochloride solution and BSTFA (N,O-bis-(trimethylsilyl)-trifluoroacetamide). The hexane and ethanol extracts of each oat cultivar were analyzed using GC-MS system. Lipid and simple sugar compositions are very similar in all samples of investigated cultivars. Chemometric tool was applied to numeric values of automatically integrated surface areas of detected lipid and simple sugar components in their corresponding derivatized forms. Hierarchical cluster analysis shows a very high similarity between the investigated flour samples of oat cultivars, according to the fatty acid content (0.9955). Moderate similarity was observed according to the content of soluble sugars (0.50). These preliminary results support the idea of establishing methods for oat flour authentication, and provide the means for distinguishing oat flour samples, regardless of the variety, from flour samples made of other cereal species, just by lipid and simple sugar profile analysis.

Keywords: Authentication, chemometrics, GC-MS, lipid and soluble sugar composition, oat cultivars.

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Authors: M. Vatani, A. R. Rahimi, F. Brazandeh, A. Sanati nezhad

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Although the STL (stereo lithography) file format is widely used as a de facto industry standard in the rapid prototyping industry due to its simplicity and ability to tessellation of almost all surfaces, but there are always some defects and shortcoming in their usage, which many of them are difficult to correct manually. In processing the complex models, size of the file and its defects grow extremely, therefore, correcting STL files become difficult. In this paper through optimizing the exiting algorithms, size of the files and memory usage of computers to process them will be reduced. In spite of type and extent of the errors in STL files, the tail-to-head searching method and analysis of the nearest distance between tails and heads techniques were used. As a result STL models sliced rapidly, and fully closed contours produced effectively and errorless.

Keywords: Layer manufacturing, STL files, slicing algorithm, nearest distance analysis.

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Authors: Maíra O. Palm, Cintia Marangoni, Ozair Souza, Noeli Sellin

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Wastes from a vegetal extracts industry (cocoa, oak, Guarana and mate) were characterized by particle size, proximate and ultimate analysis, lignocellulosic fractions, high heating value, thermal analysis (Thermogravimetric analysis – TGA, and Differential thermal analysis - DTA) and energy density to evaluate their potential as biomass in the form of briquettes for power generation. All wastes presented adequate particle sizes to briquettes production. The wastes showed high moisture content, requiring previous drying for use as briquettes. Cocoa and oak wastes had the highest volatile matter contents with maximum mass loss at 310 ºC and 450 ºC, respectively. The solvents used in the aroma extraction process influenced in the moisture content of the wastes, which was higher for mate due to water has been used as solvent. All wastes showed an insignificant loss mass after 565 °C, hence resulting in low ash content. High carbon and hydrogen contents and low sulfur and nitrogen contents were observed ensuring a low generation of sulfur and nitrous oxides. Mate and cocoa exhibited the highest carbon and lignin content, and high heating value. The dried wastes had high heating value, from 17.1 MJ/kg to 20.8 MJ/kg. The results indicate the energy potential of wastes for use as fuel in power generation.

Keywords: Agro-industrial waste, biomass, briquettes, combustion.

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Authors: Melvin Chan, Chew Lee Teo

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There has been a growing emphasis in elevating the teachers’ proficiency and competencies through continuing professional development (CPD) opportunities. In this era of a Volatile, Uncertain, Complex, Ambiguous (VUCA) world, teachers are expected to be collaborative designers, critical thinkers and creative builders. However, many of the CPD structures are still revolving in the model of transmission, which stands in contradiction to the cultivation of future-ready teachers for the innovative world of emerging technologies. This article puts forward the framing of CPD through a Principle-Based, Technological-Driven Knowledge Building Approach grounded in the essence of andragogy and progressive learning theories where growth is best exemplified through an authentic immersion in a social/community experience-based setting. Putting this Knowledge Building Professional Development Model (KBPDM) in operation via a Professional Learning Team (PLT) situated in a Secondary School in Singapore, research findings reveal that the intervention has led to a fundamental change in the learning paradigm of the teachers, henceforth equipping and empowering them successfully in their pedagogical design and practices for a 21^st century classroom experience. This article concludes with the possibility in leveraging the Learning Analytics to deepen the CPD experiences for educators.

Keywords: Continual professional development, knowledge building, learning paradigm, andragogy.

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Prevent abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep Long Short-Term Memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviours far from the expected one, depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behaviour analysis, LSTM

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Authors: Krasimir I. Ivanov, Elitsa N. Kolentsova, Dimitar Y. Dimitrov, Petya Ts. Petrova, Tatyana T. Tabakova

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This work studies the effect of chemical composition on the activity and selectivity of γ–alumina supported CuO/ MnO2/Cr2O3 catalysts toward deep oxidation of CO, dimethyl ether (DME) and methanol. The catalysts were prepared by impregnation of the support with an aqueous solution of copper nitrate, manganese nitrate and CrO3 under different conditions. Thermal, XRD and TPR analysis were performed. The catalytic measurements of single compounds oxidation were carried out on continuous flow equipment with a four-channel isothermal stainless steel reactor. Flow-line equipment with an adiabatic reactor for simultaneous oxidation of all compounds under the conditions that mimic closely the industrial ones was used. The reactant and product gases were analyzed by means of on-line gas chromatographs. On the basis of XRD analysis it can be concluded that the active component of the mixed Cu-Mn-Cr/γ–alumina catalysts consists of at least six compounds – CuO, Cr2O3, MnO2, Cu1.5Mn1.5O4, Cu1.5Cr1.5O4 and CuCr2O4, depending on the Cu/Mn/Cr molar ratio. Chemical composition strongly influences catalytic properties, this influence being quite variable with regards to the different processes. The rate of CO oxidation rapidly decrease with increasing of chromium content in the active component while for the DME was observed the reverse trend. It was concluded that the best compromise are the catalysts with Cu/(Mn + Cr) molar ratio 1:5 and Mn/Cr molar ratio from 1:3 to 1:4.

Keywords: Copper-manganese-chromium oxide catalysts, CO, deep oxidation, volatile organic compounds.

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Authors: Andrea Bencsik

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Gurus of the Classical Management School (like Taylor, Fayol and Ford) had an opinion that work must be delegated to the individual and the individual has to be instructed, his work assessed and paid based on individual performance. The theories of the Human Relations School have changed this mentality regarding the concept of groups. They came to the conclusion that the influence of groups greatly affects the behaviour and performance of its members. Group theories today are characterized by problem-solving teams and self-managing groups authorized to make decisions and execute; professional communities also play an important role during the operation of knowledge management systems. In this theoretical research we try to find answers to a question: what kind of characteristics (professional competencies, personal features, etc.) a successful team needs to manage a change to operate a knowledge management system step by step.

Keywords: Knowledge management, team, team knowledge, team memory, team roles.

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Authors: Nikolaos Kroupis, Minas Dasygenis, Dimitrios Soudris, Antonios Thanailakis

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One of the most growing areas in the embedded community is multimedia devices. Multimedia devices incorporate a number of complicated functions for their operation, like motion estimation. A multitude of different implementations have been proposed to reduce motion estimation complexity, such as spiral search. We have studied the implementations of spiral search and identified areas of improvement. We propose a modified spiral search algorithm, with lower computational complexity compared to the original spiral search. We have implemented our algorithm on an embedded ARM based architecture, with custom memory hierarchy. The resulting system yields energy consumption reduction up to 64% and performance increase up to 77%, with a small penalty of 2.3 dB, in average, of video quality compared with the original spiral search algorithm.

Keywords: Spiral Search, Motion Estimation, Embedded Systems, Low Power

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Authors: K. Ben Khalifa, M.H. Bédoui, M. Dogui, F. Alexandre

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Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable device.

Keywords: Electroencephalogram interpretation, artificialneural networks, vigilance states, hardware implementation

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Authors: Jing Zhou, Steven Su, Aihuang Guo

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COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.

Keywords: BP Neural Network, Exercising Testing, Fault Detection and Identification, Principal Component Analysis.

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Authors: Liping Zhang, Kelin Li

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In this paper, a class of impulsive BAM fuzzy cellular neural networks with time delays in the leakage terms is formulated and investigated. By establishing a delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM fuzzy cellular neural networks with time delays in the leakage terms are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM fuzzy cellular neural networks. An example is given to show the effectiveness of the results obtained here.

Keywords: Global exponential stability, bidirectional associative memory, fuzzy cellular neural networks, leakage delays, impulses.

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Authors: Monis Akhlaq, M Noman Jafri, Muzammil A Khan, Baber Aslam

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The Ad Hoc on demand distance vector (AODV) routing protocol is designed for mobile ad hoc networks (MANETs). AODV offers quick adaptation to dynamic link conditions; it is characterized by low memory overhead and low network utilization. The security issues related to the protocol remain challenging for the wireless network designers. Numerous schemes have been proposed for establishing secure communication between end users, these schemes identify that the secure operation of AODV is a bi tier task (routing and secure exchange of information at separate levels). Our endeavor in this paper would focus on achieving the routing and secure data exchange in a single step. This will facilitate the user nodes to perform routing, mutual authentications, generation and secure exchange of session key in one step thus ensuring confidentiality, integrity and authentication of data exchange in a more suitable way.

Keywords: AODV, key management, security, wirelessnetworks.

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Authors: Vishnu Kant Soni, Raksha Sharma, Manoj Kumar Mishra

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Grid computing is a high performance computing environment to solve larger scale computational applications. Grid computing contains resource management, job scheduling, security problems, information management and so on. Job scheduling is a fundamental and important issue in achieving high performance in grid computing systems. However, it is a big challenge to design an efficient scheduler and its implementation. In Grid Computing, there is a need of further improvement in Job Scheduling algorithm to schedule the light-weight or small jobs into a coarse-grained or group of jobs, which will reduce the communication time, processing time and enhance resource utilization. This Grouping strategy considers the processing power, memory-size and bandwidth requirements of each job to realize the real grid system. The experimental results demonstrate that the proposed scheduling algorithm efficiently reduces the processing time of jobs in comparison to others.

Keywords: Grid computing, Job grouping and Jobscheduling.

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Authors: Rong-Jong Wai, Chia-Ming Liu

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Generally speaking, the mobile robot is capable of sensing its surrounding environment, interpreting the sensed information to obtain the knowledge of its location and the environment, planning a real-time trajectory to reach the object. In this process, the issue of obstacle avoidance is a fundamental topic to be challenged. Thus, an adaptive path-planning control scheme is designed without detailed environmental information, large memory size and heavy computation burden in this study for the obstacle avoidance of a mobile robot. In this scheme, the robot can gradually approach its object according to the motion tracking mode, obstacle avoidance mode, self-rotation mode, and robot state selection. The effectiveness of the proposed adaptive path-planning control scheme is verified by numerical simulations of a differential-driving mobile robot under the possible occurrence of obstacle shapes.

Keywords: Adaptive Path Planning, Mobile Robot ObstacleAvoidance

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Authors: Giovanni Incerti

Abstract:

The motion planning technique described in this paper has been developed to eliminate or reduce the residual vibrations of belt-driven rotary platforms, while maintaining unchanged the motion time and the total angular displacement of the platform. The proposed approach is based on a suitable choice of the motion command given to the servomotor that drives the mechanical device; this command is defined by some numerical coefficients which determine the shape of the displacement, velocity and acceleration profiles. Using a numerical optimization technique, these coefficients can be changed without altering the continuity conditions imposed on the displacement and its time derivatives at the initial and final time instants. The proposed technique can be easily and quickly implemented on an actual device, since it requires only a simple modification of the motion command profile mapped in the memory of the electronic motion controller.

Keywords: Command shaping, residual vibrations, belt transmission, servomechanism.

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Authors: Mikel Alonso López, María Francisca Blasco López, Víctor Molero Ayala

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The present study explains the somatic marker theory of Antonio Damasio, which indicates that when making a decision, the stored or possible future scenarios (future memory) images allow people to feel for a moment what would happen when they make a choice, and how this is emotionally marked. This process can be conscious or unconscious. The development of new Neuromarketing techniques such as functional magnetic resonance imaging (fMRI), carries a greater understanding of how the brain functions and consumer behavior. In the results observed in different studies using fMRI, the evidence suggests that the somatic marker and future memories influence the decision-making process, adding a positive or negative emotional component to the options. This would mean that all decisions would involve a present emotional component, with a rational cost-benefit analysis that can be performed later.

Keywords: Emotions, decision making, somatic marker, consumer´s brain.

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Authors: N. Hadjigeorgiou, A. C. Tsalikidou, E. Hristoforou, P. P. Sotiriadis

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During the last few decades, the continuously increasing demand for accurate and reliable magnetic measurements has paved the way for the development of different types of magnetic sensing systems as well as different measurement techniques. Sensor sensitivity and linearity, signal-to-noise ratio, measurement range, cross-talk between sensors in multi-sensor applications are only some of the aspects that have been examined in the past. In this paper, a fully analog closed loop system in order to optimize the performance of AMR sensors has been developed. The operation of the proposed system has been tested using a Helmholtz coil calibration setup in order to control both the amplitude and direction of magnetic field in the vicinity of the AMR sensor. Experimental testing indicated that improved linearity of sensor response, as well as low noise levels can be achieved, when the system is employed.

Keywords: AMR sensor, closed loop, memory effects, chopper, linearity improvement, sensitivity improvement, magnetic noise, electronic noise.

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Authors: Mario Mastriani

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Super-resolution is nowadays used for a high-resolution image produced from several low-resolution noisy frames. In this work, we consider the problem of high-quality interpolation of a single noise-free image. Such images may come from different sources, i.e., they may be frames of videos, individual pictures, etc. On the other hand, in the encoder we apply a downsampling via bidimen-sional interpolation of each frame, and in the decoder we apply a upsampling by which we restore the original size of the image. If the compression ratio is very high, then we use a convolutive mask that restores the edges, eliminating the blur. Finally, both, the encoder and the complete decoder are implemented on General-Purpose computation on Graphics Processing Units (GPGPU) cards. In fact, the mentioned mask is coded inside texture memory of a GPGPU.

Keywords: General-Purpose computation on Graphics ProcessingUnits, Image Compression, Interpolation, Super-resolution.

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Authors: Zanariah Abdul Majid, Mohamed Suleiman

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The aim of this paper is to investigate the performance of the developed two point block method designed for two processors for solving directly non stiff large systems of higher order ordinary differential equations (ODEs). The method calculates the numerical solution at two points simultaneously and produces two new equally spaced solution values within a block and it is possible to assign the computational tasks at each time step to a single processor. The algorithm of the method was developed in C language and the parallel computation was done on a parallel shared memory environment. Numerical results are given to compare the efficiency of the developed method to the sequential timing. For large problems, the parallel implementation produced 1.95 speed-up and 98% efficiency for the two processors.

Keywords: Numerical methods, parallel method, block method, higher order ODEs.

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Authors: Jin-Tsann Yeh, Chyong-Ling Lin

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Healthcare providers sometimes use the power of humor as a treatment and therapy for buffering mental health or easing mental disorders because humor can provide relief from distress and conflict. Humor is also very suitable for advertising because of similar benefits. This study carefully examines humor's widespread use in advertising and identifies relationships among humor mechanisms, female depictions, and product types. The purpose is to conceptualize how humor theories can be used not only to successfully define a product as fitting within one of four color categories of the product color matrix, but also to identify compelling contemporary female depictions through humor in ads. The results can offer an idealization for marketing managers and consumers to help them understand how female role depictions can be effectively used with humor in ads. The four propositions developed herein are derived from related literature, through the identification of marketing strategy formulations that achieve product memory enhancement by adopting humor mechanisms properly matched with female role depictions.

Keywords: Humor mechanisms, Female role depiction, Product types.

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