Search results for: Adaptive genetic algorithm

Authors: U. Ramesh Babu, V. Vijay Kumar Reddy, S. Tara Kalyani

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The growth in the demand of electrical energy is leading to load on the Power system which increases the occurrence of frequent oscillations in the system. The reason for the oscillations is due to the lack of damping torque which is required to dominate the disturbances of Power system. By using FACT devices, such as Unified Power Flow Controller (UPFC) can control power flow, reduce sub-synchronous resonances and increase transient stability. Hence, UPFC is used to damp the oscillations occurred in Power system. This research focuses on adapting the neuro fuzzy controller for the UPFC design by connecting the infinite bus (SMIB - Single machine Infinite Bus) to a linearized model of synchronous machine (Heffron-Phillips) in the power system. This model gains the capability to improve the transient stability and to damp the oscillations of the system.

Keywords: Power System, UPFC, (ANFIS) Adaptive Neuro Fuzzy Inference System, transient, Low frequency oscillations.

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Authors: Ashraf Anwar

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In this work, I present a review on Sparse Distributed Memory for Small Cues (SDMSCue), a variant of Sparse Distributed Memory (SDM) that is capable of handling small cues. I then conduct and show some cognitive experiments on SDMSCue to test its cognitive soundness compared to SDM. Small cues refer to input cues that are presented to memory for reading associations; but have many missing parts or fields from them. The original SDM failed to handle such a problem. SDMSCue handles and overcomes this pitfall. The main idea in SDMSCue; is the repeated projection of the semantic space on smaller subspaces; that are selected based on the input cue length and pattern. This process allows for Read/Write operations using an input cue that is missing a large portion. SDMSCue is augmented with the use of genetic algorithms for memory allocation and initialization. I claim that SDM functionality is a subset of SDMSCue functionality.

Keywords: Artificial intelligence, recall, recognition, SDM, SDMSCue.

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Authors: Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-Nearest Neighbors, Neural Network, Radial Basis Function, Red blood cells, Support vector machine.

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Authors: Saeed Mian Qaisar, Laurent Fesquet, Marc Renaudin

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The mobile systems are powered by batteries. Reducing the system power consumption is a key to increase its autonomy. It is known that mostly the systems are dealing with time varying signals. Thus, we aim to achieve power efficiency by smartly adapting the system processing activity in accordance with the input signal local characteristics. It is done by completely rethinking the processing chain, by adopting signal driven sampling and processing. In this context, a signal driven filtering technique, based on the level crossing sampling is devised. It adapts the sampling frequency and the filter order by analysing the input signal local variations. Thus, it correlates the processing activity with the signal variations. It leads towards a drastic computational gain of the proposed technique compared to the classical one.

Keywords: Level Crossing Sampling, Activity Selection, Adaptive Rate Filtering, Computational Complexity.

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Authors: Shuen-Tai Wang, Hsi-Ya Chang

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Cloud virtualization technologies are becoming more and more prevalent, cloud users usually encounter the problem of how to access to the virtualized remote desktops easily over the web without requiring the installation of special clients. To resolve this issue, we took advantage of the HTML5 technology and developed web-based remote desktop. It permits users to access the terminal which running in our cloud platform from anywhere. We implemented a sketch of web interface following the cloud computing concept that seeks to enable collaboration and communication among users for high performance computing. Given the development of remote desktop virtualization, it allows to shift the user’s desktop from the traditional PC environment to the cloud platform, which is stored on a remote virtual machine rather than locally. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for online cloud service. This is also made possible by the low administrative costs as well as relatively inexpensive end-user terminals and reduced energy expenses.

Keywords: Virtualization, Remote Desktop, HTML5, Cloud Computing.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

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End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: Sui Xin, Li Chunsheng, Tian Di

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Short message integrated distributed monitoring systems (SM-DMS) are growing rapidly in wireless communication applications in various areas, such as electromagnetic field (EMF) management, wastewater monitoring, and air pollution supervision, etc. However, delay in short messages often makes the data embedded in SM-DMS transmit unreliably. Moreover, there are few regulations dealing with this problem in SMS transmission protocols. In this study, based on the analysis of the command and data requirements in the SM-DMS, we developed a processing model for the control center to solve the delay problem in data transmission. Three components of the model: the data transmission protocol, the receiving buffer pool method, and the timer mechanism were described in detail. Discussions on adjusting the threshold parameter in the timer mechanism were presented for the adaptive performance during the runtime of the SM-DMS. This model optimized the data transmission reliability in SM-DMS, and provided a supplement to the data transmission reliability protocols at the application level.

Keywords: Delay, SMS, reliability, distributed monitoringsystem (DMS), wireless communication.

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Authors: Walaa M.Abd-Elhafiez, Wajeb Gharibi

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In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into the edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique that is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio.

Keywords: Image compression, color image, Q-coder, quantization, edge-detection.

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Authors: Yunyong Guo, Sudhakar Ganti, Bryan Guo

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The rapid growth of telehealth Internet of Things (IoT) devices has raised concerns about energy consumption and efficient data processing. This paper presents an energy-efficient model that integrates telehealth IoT devices with a fog and cloud computing-based platform, offering a sustainable and robust solution to overcome these challenges. Our model employs fog computing as a localized data processing layer while leveraging cloud computing for resource-intensive tasks, significantly reducing energy consumption. We incorporate adaptive energy-saving strategies. Simulation analysis validates our approach's effectiveness in enhancing energy efficiency for telehealth IoT systems integrated with localized fog nodes and both private and public cloud infrastructures. Future research will focus on further optimization of the energy-saving model, exploring additional functional enhancements, and assessing its broader applicability in other healthcare and industry sectors.

Keywords: Energy-efficient, fog computing, IoT, telehealth.

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Authors: Luminita Dumitriu, Cristina Segal, Marian Craciun, Adina Cocu, Lucian P. Georgescu

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There are several approaches in trying to solve the Quantitative 1Structure-Activity Relationship (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.

Keywords: association rules, classification, data mining, Quantitative Structure - Activity Relationship.

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Authors: Huda Algharib, Amal Algharib, Hanan Algharib, Ali Mohammad Alqudah

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Image registration is an important topic for many imaging systems and computer vision applications. The standard image registration techniques such as Mutual information/ Normalized mutual information -based methods have a limited performance because they do not consider the spatial information or the relationships between the neighbouring pixels or voxels. In addition, the amount of image noise may significantly affect the registration accuracy. Therefore, this paper proposes an efficient method that explicitly considers the relationships between the adjacent pixels, where the gradient information of the reference and scene images is extracted first, and then the cosine similarity of the extracted gradient information is computed and used to improve the accuracy of the standard normalized mutual information measure. Our experimental results on different data types (i.e. CT, MRI and thermal images) show that the proposed method outperforms a number of image registration techniques in terms of the accuracy.

Keywords: Image registration, mutual information, image gradients, Image transformations.

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Authors: Wojciech P. Gajewski

Abstract:

The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag Of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without affecting the classifier precision as it happens when only the NBC based on single words is retrained.

Keywords: Text classification, naïve Bayesian classification, spam, email.

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Authors: Ákos Malatinszky, Szilvia Ádám

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Climate change causes severe effects on natural habitats, especially wetlands. These challenges require the adaptation of their management to probable effects of climate change. A compilation of necessary changes in land management was collected in a Hungarian area being both national park and Natura 2000 SAC and SCI site in favor of increasing the resilience and reducing vulnerability. Several factors, such as ecological aspects, nature conservation and climatic adaptation should be combined with social and economic factors during the process of developing climate change adapted management on vulnerable wetlands. Planning adaptive management should be determined by a priority order of conservation aims and evaluation of factors at the determined planning unit. Mowing techniques, frequency and exact date should be observed as well as grazing species and their breed, due to different grazing, group forming and trampling habits. Integrating landscape history and historical land development into the planning process is essential.

Keywords: Adaptation, climate change, management, wetland.

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Authors: El Falaki Brahim, Khalidi Idrissi Mohammed, Bennani Samir

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E-learning is not restricted to the use of new technologies for the online content, but also induces the adoption of new approaches to improve the quality of education. This quality depends on the ability of these approaches (technical and pedagogical) to provide an adaptive learning environment. Thus, the environment should include features that convey intentions and meeting the educational needs of learners by providing a customized learning path to acquiring a competency concerned In our proposal, we believe that an individualized learning path requires knowledge of the learner. Therefore, it must pass through a personalization of diagnosis to identify precisely the competency gaps to fill, and reduce the cognitive load To personalize the diagnosis and pertinently measure the competency gap, we suggest implementing the formative assessment in the e-learning environment and we propose the introduction of a pre-regulation process in the area of formative assessment, involving its individualization and implementation in e-learning.

Keywords: Competency-Based-Approach, E-learning, Formative assessment, learner model, Modeling, pre-regulation process

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Authors: L. McAllister, A. Dudau

Abstract:

This paper explores gender related barriers to interagency collaboration in statutory children safeguard partnerships against a theoretical framework that considers individuals, professions and organisations interacting as part of a complex adaptive system. We argue that gender-framed obstacles to effective communication between culturally discrepant agencies can ultimately impact on the effectiveness of policy delivery,. We focused our research on three partnership structures in Sefton Metropolitan Borough in order to observe how interactions occur, whether the agencies involved perceive their occupational environment as being gender affected and whether they believe this can hinder effective collaboration with other biased organisations. Our principal empirical findings indicate that there is a general awareness amongst professionals of the role that gender plays in each of the agencies reviewed, that gender may well constitute a barrier to effective communication, but there is a sense in which there is little scope for change in the short term. We aim to signal here, however, the need to change against the risk of service failure.

Keywords: Children's safeguard, gender, gendered professions, inter-agency collaboration, partnerships.

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Authors: Salam Saudagar, Ankit Kamboj, Niraj Mohan, Satgounda Patil, Nilesh Powar

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Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.

Keywords: Assignment, deadline, greedy approach, hungarian algorithm, operations research, scheduling.

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Authors: D. Laiadi, A. Hasseine, A. Merzougui

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In this work, Experimental tie-line results and solubility (binodal) curves were obtained for the ternary systems (water + acetic acid + methyl isobutyl ketone (MIBK)), (water + lactic acid+ methyl isobutyl ketone) at T = 294.15K and atmospheric pressure. The consistency of the values of the experimental tie-lines was determined through the Othmer-Tobias and Hands correlations. For the extraction effectiveness of solvents, the distribution and selectivity curves were plotted. In addition, these experimental tieline data were also correlated with NRTL model. The interaction parameters for the NRTL model were retrieved from the obtained experimental results by means of a combination of the homotopy method and the genetic algorithms.

Keywords: Liquid-liquid equilibria, homotopy methods, carboxylic acid, NRTL.

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Authors: S. Jalaja, A. M. Vijaya Prakash

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Recursive combination of an algorithm based on Karatsuba multiplication is exploited to design a generalized transpose and parallel Finite Impulse Response (FIR) Filter. Mid-range Karatsuba multiplication and Carry Save adder based on Karatsuba multiplication reduce time complexity for higher order multiplication implemented up to n-bit. As a result, we design modified N-tap Transpose and Parallel Symmetric FIR Filter Structure using Karatsuba algorithm. The mathematical formulation of the FFA Filter is derived. The proposed architecture involves significantly less area delay product (APD) then the existing block implementation. By adopting retiming technique, hardware cost is reduced further. The filter architecture is designed by using 90 nm technology library and is implemented by using cadence EDA Tool. The synthesized result shows better performance for different word length and block size. The design achieves switching activity reduction and low power consumption by applying with and without retiming for different combination of the circuit. The proposed structure achieves more than a half of the power reduction by adopting with and without retiming techniques compared to the earlier design structure. As a proof of the concept for block size 16 and filter length 64 for CKA method, it achieves a 51% as well as 70% less power by applying retiming technique, and for CSA method it achieves a 57% as well as 77% less power by applying retiming technique compared to the previously proposed design.

Keywords: Carry save adder Karatsuba multiplication, mid-range Karatsuba multiplication, modified FFA, transposed filter, retiming.

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Authors: Olayiwola M.O., Gbolagade A .W., Akinpelu F. O.

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In this work, we present a reliable framework to solve boundary value problems with particular significance in solid mechanics. These problems are used as mathematical models in deformation of beams. The algorithm rests mainly on a relatively new technique, the Variational Iteration Method. Some examples are given to confirm the efficiency and the accuracy of the method.

Keywords: Variational iteration method, boundary value problems, convergence, restricted variation.

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Authors: Adam Kurnicki, Bartłomiej Stanczyk, Bartosz Kania

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This paper presents development of the light-weight manipulator with series elastic actuation for medical telediagnostics (USG examination). General structure of realized impedance control algorithm was shown. It was described how to perform force measurements based mainly on elasticity of manipulator links.

Keywords: Telediagnostics, elastic manipulator, impedance control, force measurement.

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Authors: J. Prakash, K. Rajesh

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In this paper, we introduce a new method for elliptical object identification. The proposed method adopts a hybrid scheme which consists of Eigen values of covariance matrices, Circular Hough transform and Bresenham-s raster scan algorithms. In this approach we use the fact that the large Eigen values and small Eigen values of covariance matrices are associated with the major and minor axial lengths of the ellipse. The centre location of the ellipse can be identified using circular Hough transform (CHT). Sparse matrix technique is used to perform CHT. Since sparse matrices squeeze zero elements and contain a small number of nonzero elements they provide an advantage of matrix storage space and computational time. Neighborhood suppression scheme is used to find the valid Hough peaks. The accurate position of circumference pixels is identified using raster scan algorithm which uses the geometrical symmetry property. This method does not require the evaluation of tangents or curvature of edge contours, which are generally very sensitive to noise working conditions. The proposed method has the advantages of small storage, high speed and accuracy in identifying the feature. The new method has been tested on both synthetic and real images. Several experiments have been conducted on various images with considerable background noise to reveal the efficacy and robustness. Experimental results about the accuracy of the proposed method, comparisons with Hough transform and its variants and other tangential based methods are reported.

Keywords: Circular Hough transform, covariance matrix, Eigen values, ellipse detection, raster scan algorithm.

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Authors: Abdalla Kablan, Joseph Falzon

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This paper is motivated by the aspect of uncertainty in financial decision making, and how artificial intelligence and soft computing, with its uncertainty reducing aspects can be used for algorithmic trading applications that trade in high frequency. This paper presents an optimized high frequency trading system that has been combined with various moving averages to produce a hybrid system that outperforms trading systems that rely solely on moving averages. The paper optimizes an adaptive neuro-fuzzy inference system that takes both the price and its moving average as input, learns to predict price movements from training data consisting of intraday data, dynamically switches between the best performing moving averages, and performs decision making of when to buy or sell a certain currency in high frequency.

Keywords: Financial decision making, High frequency trading, Adaprive neuro-fuzzy systems, moving average strategy.

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Authors: M. Chňapek, M. Tomka, R. Peroutková, Z. Gálová

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Processes of plant breeding, testing and licensing of new varieties, patent protection in seed production, relations in trade and protection of copyright are dependent on identification, differentiation and characterization of plant genotypes. Therefore, we focused our research on utilization of wheat storage proteins as genetic markers suitable not only for differentiation of individual genotypes, but also for identification and characterization of their considerable properties. We analyzed a collection of 102 genotypes of bread wheat (Triticum aestivum L.), 41 genotypes of spelt wheat (Triticum spelta L.), and 35 genotypes of durum wheat (Triticum durum Desf.), in this study. Our results show, that genotypes of bread wheat and durum wheat were homogenous and single line, but spelt wheat genotypes were heterogenous. We observed variability of HMW-GS composition according to environmental factors and level of breeding and predict technological quality on the basis of Glu-score calculation.

Keywords: Genotype identification, HMW-GS, wheat quality.

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Authors: A. Rong, P. B. Luh, R. Lahdelma

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High penetration of intermittent renewable energy sources (RES) such as solar power and wind power into the energy system has caused temporal and spatial imbalance between electric power supply and demand for some countries and regions. This brings about the critical need for coordinating power production and power exchange for different regions. As compared with the power-only systems, the combined heat and power (CHP) systems can provide additional flexibility of utilizing RES by exploiting the interdependence of power and heat production in the CHP plant. In the CHP system, power production can be influenced by adjusting heat production level and electric power can be used to satisfy heat demand by electric boiler or heat pump in conjunction with heat storage, which is much cheaper than electric storage. This paper addresses multi-site CHP systems without considering RES, which lay foundation for handling penetration of RES. The problem under study is the unit commitment (UC) of the transmission-constrained multi-site CHP systems. We solve the problem by combining linear relaxation of ON/OFF states and sequential dynamic programming (DP) techniques, where relaxed states are used to reduce the dimension of the UC problem and DP for improving the solution quality. Numerical results for daily scheduling with realistic models and data show that DP-based algorithm is from a few to a few hundred times faster than CPLEX (standard commercial optimization software) with good solution accuracy (less than 1% relative gap from the optimal solution on the average).

Keywords: Dynamic programming, multi-site combined heat and power system, relaxed states, transmission-constrained generation unit commitment.

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Authors: Yong-Sung Kim, Je-Seok Shin, Ho-Jun Jo Jin-O Kim

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Home Energy Management System (HEMS), which makes the residential consumers, contribute to the demand response is attracting attention in recent years. An aim of HEMS is to minimize their electricity cost by controlling the use of their appliances according to electricity price. The use of appliances in HEMS may be affected by some conditions such as external temperature and electricity price. Therefore, the user’s usage pattern of appliances should be modeled according to the external conditions, and the resultant usage pattern is related to the user’s comfortability on use of each appliances. This paper proposes a methodology to model the usage pattern based on the historical data with the copula function. Through copula function, the usage range of each appliance can be obtained and is able to satisfy the appropriate user’s comfort according to the external conditions for next day. Within the usage range, an optimal scheduling for appliances would be conducted so as to minimize an electricity cost with considering user’s comfort. Among the home appliance, electric heater (EH) is a representative appliance, which is affected by the external temperature. In this paper, an optimal scheduling algorithm for an electric heater (EH) is addressed based on the method of branch and bound. As a result, scenarios for the EH usage are obtained according to user’s comfort levels and then the residential consumer would select the best scenario. The case study shows the effects of the proposed algorithm compared with the traditional operation of the EH, and it represents impacts of the comfort level on the scheduling result.

Keywords: Load scheduling, usage pattern, user’s comfort, copula function, branch, bound, electric heater.

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Authors: Zenaide Carvalho da Silva, Andrey Ricardo Pimentel, Leandro Rodrigues Ferreira

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Learning styles (LSs) correspond to the individual preferences of a person regarding the modes and forms in which he/she prefers to learn throughout the teaching/learning process. The content presentation of learning objects (LOs) using knowledge about the students’ LSs offers them digital educational resources tailored to their individual learning preferences. In this context, the most relevant characteristics of the LSs along with the most appropriate forms of LOs' content presentation were mapped and associated. Such was performed in order to define the composition of an adaptive model of LO's content presentation considering the LSs, which was called Adaptation of Content Presentation of Learning Objects Considering Learning Styles (ACPLOLS). LO prototypes were created with interfaces that were adapted to students' LSs. These prototypes were based on a model created for validation of the approaches that were used, which were established through experiments with the students. The results of subjective measures of students' emotional responses demonstrated that the ACPLOLS has reached the desired results in relation to the adequacy of the LOs interface, in accordance with the Felder-Silverman LSs Model.

Keywords: Adaptation, interface, learning styles, learning objects, students.

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Authors: Nuha Omran Abokhdair, Azizah Bt Abdul Manaf

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Reversible watermarking is a special branch of image watermarking, that is able to recover the original image after extracting the watermark from the image. In this paper, an adaptive prediction-based reversible watermarking scheme is presented, in order to increase the payload capacity of MRI medical images. The scheme divides the image into two parts, Region of Interest (ROI) and Region of Non-Interest (RONI). Two bits are embedded in each embeddable pixel of RONI and one bit is embedded in each embeddable pixel of ROI. The experimental results demonstrate that the proposed scheme is able to achieve high embedding capacity. This is mainly caused by two reasons. First, the pixels that were excluded from data embedding due to overflow/underflow are used for data embedding. Second, large location map that need to be added to watermark data as overhead is eliminated and thus lower data embedding capacity is prevented. Moreover, the scheme provides good visual quality to the watermarked image.

Keywords: Medical image watermarking, reversible watermarking, Difference Expansion, Prediction-Error Expansion.

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Authors: V. H. Mankar, T. S. Das, S. K. Sarkar

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In this paper, we have proposed a novel blind watermarking architecture towards its hardware implementation in VLSI. In order to facilitate this hardware realization, cellular automata (CA) concept is introduced. The CA has been already accepted as an attractive structure for VLSI implementation because of its modularity, parallelism, high performance and reliability. The hardware realizable multiresolution spread spectrum watermarking techniques are very few in numbers in spite of their best ever resiliency against signal impairments. This is because of the computational cost and complexity associated with their different filter banks and lifting techniques. The concept of cellular automata theory in order to form a new transform domain technique i.e. Cellular Automata Transform (CAT) have been incorporated. Since CA provides spreading sequences having very low cross-correlation properties, the CA based pseudorandom sequence generator is considered in the present work. Considering the watermarking technique as a digital communication process, an error control coding (ECC) must be incorporated in the data hiding schemes. Besides the hardware implementation of entire CA based data hiding technique, the individual blocks of the algorithm using CA provide the best result than that of some other methods irrespective of the hardware and software technique. The Cellular Automata Transform, CA based PN sequence generator, and CA ECC are the requisite blocks that are developed not only to meet the reliable hardware requirements but also for the basic spread spectrum watermarking features. The proposed algorithm shows statistical invisibility and resiliency against various common signal-processing operations. This algorithmic design utilizes the existing allocated bandwidth in the data transmission channel in a more efficient manner.

Keywords: Cellular automata, watermarking, error control coding, PN sequence, VLSI.

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Authors: Yanju Zhang, Joost M. Woltering, Fons J. Verbeek

Abstract:

It has been established that microRNAs (miRNAs) play an important role in gene expression by post-transcriptional regulation of messengerRNAs (mRNAs). However, the precise relationships between microRNAs and their target genes in sense of numbers, types and biological relevance remain largely unclear. Dissecting the miRNA-target relationships will render more insights for miRNA targets identification and validation therefore promote the understanding of miRNA function. In miRBase, miRanda is the key algorithm used for target prediction for Zebrafish. This algorithm is high-throughput but brings lots of false positives (noise). Since validation of a large scale of targets through laboratory experiments is very time consuming, several computational methods for miRNA targets validation should be developed. In this paper, we present an integrative method to investigate several aspects of the relationships between miRNAs and their targets with the final purpose of extracting high confident targets from miRanda predicted targets pool. This is achieved by using the techniques ranging from statistical tests to clustering and association rules. Our research focuses on Zebrafish. It was found that validated targets do not necessarily associate with the highest sequence matching. Besides, for some miRNA families, the frequency of their predicted targets is significantly higher in the genomic region nearby their own physical location. Finally, in a case study of dre-miR-10 and dre-miR-196, it was found that the predicted target genes hoxd13a, hoxd11a, hoxd10a and hoxc4a of dre-miR- 10 while hoxa9a, hoxc8a and hoxa13a of dre-miR-196 have similar characteristics as validated target genes and therefore represent high confidence target candidates.

Keywords: MicroRNA targets validation, microRNA-target relationships, dre-miR-10, dre-miR-196.

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Authors: Inês Ramalhete, Hugo Farias, Rui da Silva Pinto

Abstract:

Adequate housing has been a widely discussed theme in academic circles related to low-cost housing, whereas its physical features are easy to deal with, overcrowding (related to social, cultural and economic aspects) is still ambiguous, particularly regarding the set of indicators that can accurately reflect and measure it. This paper develops research on low-cost housing models for developing countries and what is the best method to embed overcrowding as an important parameter for adaptability. A critical review of international overcrowding indicators and their application in two developing countries, Cape Verde and Angola, is presented. The several rationales and the constraints for an accurate assessment of overcrowding are considered, namely baseline data (statistics), which can induce misjudgments, as well as social and cultural factors (such as personal choices of residents). This paper proposes a way to tackle overcrowding through housing adaptability, considering factors such as physical flexibility, functional ambiguity, and incremental expansion schemes. Moreover, a case-study is presented to establish a framework for the theoretical application of the proposed approach.

Keywords: Adaptive housing, low-cost housing, overcrowding.

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