title: A Voronoi Diagram Based Classifier for Multiclass Imbalanced Data Sets

abstract: The imbalance problem is receiving an increasing attention in the literature. Studies in binary cases are recurrent, however there still are several real world problems with more than two classes. The known solutions for binary datasets may not be applicable in this case. Some efforts are being applied in decomposition techniques which transforms a multiclass problem into some binary problems. However it is also possible to face a multiclass problem with an ad hoc approach, i.e., a classifier able to handle all classes at once. In this work a method able to handle several classes is proposed. This new method is based on the Voronoi diagram. We try to dynamically divide the feature space into several regions, each one assigned to a different class. It is expected for the method to be able to construct a complex classification model. However, as it is in its beginning, some tests need to be performed in order to evaluate its feasibility. Experiments with some classical classifiers confirm its feasibility, and comparisons with ad hoc methods found in literature show its potentiality.


title: Cuff-less and Noninvasive Measurements of Arterial Blood Pressure by Pulse Transit Time

abstract: The current blood pressure (BP) measurement devices are mostly built on the principle of auscultation, oscillometry or tonometry, all of which use an inflatable cuff to occlude or unload the artery. The need of a cuff in these devices limits the further reduction in size and power consumption, and restricts the frequency and ease of their usage. Therefore, this study aims to develop a cuff-less and noninvasive technique for measuring BP by pulse transit time. The technique was evaluated on 85 subjects, aged 57+/-29 yrs., including 36 males and 39 hypertensives, over an average period of 6.4 wks. A total of 999 pairs of systolic BP (SBP) and diastolic BP (DBP) estimations were made. The average of BP readings reported by an experienced registered nurse and those obtained from a clinically approved automatic BP meter was used as reference. It is found that the estimated SBP and DBP differed from the reference BP by 0.6+/-9.8mmHg and 0.9+/-5.6mmHg respectively. When compared to the AAMI requirement (5+/-8mmHg for both SBP and DBP estimation), the results suggest that the cuff-less technology has great potential to be developed into wearable devices that are useful in self BP monitoring for home healthcare and eventually in clinical diagnosis.


title: Distributed ray tracing

abstract: Ray tracing is one of the most elegant techniques in computer graphics. Many phenomena that are difficult or impossible with other techniques are simple with ray tracing, including shadows, reflections, and refracted light. Ray directions, however, have been determined precisely, and this has limited the capabilities of ray tracing. By distributing the directions of the rays according to the analytic function they sample, ray tracing can incorporate fuzzy phenomena. This provides correct and easy solutions to some previously unsolved or partially solved problems, including motion blur, depth of field, penumbras, translucency, and fuzzy reflections. Motion blur and depth of field calculations can be integrated with the visible surface calculations, avoiding the problems found in previous methods.


title: ELM ∗ : distributed extreme learning machine with MapReduce

abstract: Extreme Learning Machine (ELM) has been widely used in many fields such as text classification, image recognition and bioinformatics, as it provides good generalization performance at a extremely fast learning speed. However, as the data volume in real-world applications becomes larger and larger, the traditional centralized ELM cannot learn such massive data efficiently. Therefore, in this paper, we propose a novel Distributed Extreme Learning Machine based on MapReduce framework, named ELM???, which can cover the shortage of traditional ELM whose learning ability is weak to huge dataset. Firstly, after adequately analyzing the property of traditional ELM, it can be found out that the most expensive computation part of the matrix Moore-Penrose generalized inverse operator in the output weight vector calculation is the matrix multiplication operator. Then, as the matrix multiplication operator is decomposable, a Distributed Extreme Learning Machine (ELM???) based on MapReduce framework can be developed, which can first calculate the matrix multiplication effectively with MapReduce in parallel, and then calculate the corresponding output weight vector with centralized computing. Therefore, the learning of massive data can be made effectively. Finally, we conduct extensive experiments on synthetic data to verify the effectiveness and efficiency of our proposed ELM??? in learning massive data with various experimental settings.


title: Implementation of Minutiae Based Fingerprint Identification System Using Crossing Number Concept

abstract: Biometric system is essentially a pattern recognition system which recognizes a person by determining the authenticity of a specific physiological (e.g., fingerprints, face, retina, iris) or behavioral (e.g., gait, signature) characteristic possessed by that person. Among all the presently employed biometric techniques, fingerprint identification systems have received the most attention due to the long history of fingerprints and its extensive use in forensics. Fingerprint is reliable biometric characteristic as it is unique and persistence. Fingerprint is the pattern of ridges and valleys on the surface of fingertip. However, recognizing fingerprints in poor quality images is still a very complex job, so the fingerprint image must be preprocessed before matching. It is very difficult to extract fingerprint features directly from grey scale fingerprint image. In this paper we have proposed the system which uses minutiae based matching algorithm for fingerprint identification. There are three main phases in proposed algorithm. First phase enhance the input fingerprint image by preprocessing it. The enhanced fingerprint image is converted into thinned binary image and then minutiae are extracted by using Crossing Number Concept in second phase. Third stage compares input fingerprint image (after preprocessing and minutiae extraction) with fingerprint images enrolled in database and makes decision whether the input fingerprint is matched with the fingerprint stored in database or not.


title: HL7 middleware framework for healthcare information system

abstract: In this paper, a new middleware framework developed for the Health Information System (HIS) rightsizing project of National Taiwan University Hospital (NTUH) is proposed. The framework is basically a Service-Oriented Architecture (SOA). Challenges as formatting complex medical information as well as integrating heterogeneous systems in our hospital are addressed by introducing HL7 and Web services standard into our framework. Finally, the performance of a operating HIS based on our framework is analyzed and presented to evaluate the efficiency of our design. Keywords—HL7, Healthcare information system, web services, Service-oriented Architecture


title: Non-verbal communication of compassion: measuring psychophysiologic effects

abstract: BackgroundCalm, compassionate clinicians comfort others. To evaluate the direct psychophysiologic benefits of non-verbal communication of compassion (NVCC), it is important to minimize the effect of subjects' expectation. This preliminary study was designed to a) test the feasibility of two strategies for maintaining subject blinding to non-verbal communication of compassion (NVCC), and b) determine whether blinded subjects would experience psychophysiologic effects from NVCC.MethodsSubjects were healthy volunteers who were told the study was evaluating the effect of time and touch on the autonomic nervous system. The practitioner had more than 10 years' experience with loving-kindness meditation (LKM), a form of NVCC. Subjects completed 10-point visual analog scales (VAS) for stress, relaxation, and peacefulness before and after LKM. To assess physiologic effects, practitioners and subjects wore cardiorespiratory monitors to assess respiratory rate (RR), heart rate (HR) and heart rate variability (HRV) throughout the 4 10-minute study periods: Baseline (both practitioner and subjects read neutral material); non-tactile-LKM (subjects read while the practitioner practiced LKM while pretending to read); tactile-LKM (subjects rested while the practitioner practiced LKM while lightly touching the subject on arms, shoulders, hands, feet, and legs); Post-Intervention Rest (subjects rested; the practitioner read). To assess blinding, subjects were asked after the interventions what the practitioner was doing during each period (reading, touch, or something else).ResultsSubjects' mean age was 43.6 years; all were women. Blinding was maintained and the practitioner was able to maintain meditation for both tactile and non-tactile LKM interventions as reflected in significantly reduced RR. Despite blinding, subjects' VAS scores improved from baseline to post-intervention for stress (5.5 vs. 2.2), relaxation (3.8 vs. 8.8) and peacefulness (3.8 vs. 9.0, P < 0.05 for all comparisons). Subjects also had significant reductions in RR (P < 0.0001) and improved HRV (P < 0.05) with both tactile and non-tactile LKM.ConclusionIt is possible to test the effects of LKM with tactile and non-tactile blinding strategies; even with blinding in this small preliminary study, subjects reported significant improvements in well-being which were reflected in objective physiologic measures of autonomic activity. Extending compassion is not only good care; it may also be good medicine.Trial registration numberUS National ClinicalTrials.gov registration number, NCT01428674


title: Rule Inference for Financial Prediction using Recurrent Neural Networks

abstract: The paper considers the prediction of noisy time series data, specifically, the prediction of foreign exchange rate data. A novel hybrid neural network algorithm for noisy time series prediction is presented which exhibits excellent performance on the problem. The method is motivated by consideration of how neural networks work, and by fundamental difficulties with random correlations when dealing with small sample sizes and high noise data. The method permits the inference and extraction of rules. One of the greatest complaints against neural networks is that it is hard to figure out exactly what they are doing-this work provides one answer for the internal workings of the network. Furthermore, these rules can be used to gain insight into both the real world system and the predictor. The paper focuses on noisy time series prediction and rule inference-use of the system in trading would typically involve the utilization of other financial indicators and domain knowledge.


title: An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem

abstract: The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-bound algorithm to solve the problem. The branch-and-bound algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our algorithm is superior to the current best exact algorithm. Finally, the impacts of different parameter settings, algorithm components and instance generation distributions are disclosed by some additional experiments.


title: Active sample selection in scalar fields exhibiting non-stationary noise with parametric heteroscedastic Gaussian process regression

abstract: This paper considers the modelling of scalar fields exhibiting non-stationary noise in the context of Gaussian Process (GP) regression. We show how a Heteroscedastic GP produces more accurate predictions of the variance of a process of this type compared to the standard Homoscedastic model. We present a parametric model for the noise process and derive analytical solutions to the Log Marginal Likelihood of the data and its gradients with respect to Hyper Parameters of the kernel and the noise process. We compare our parametric model to one which estimates a full GP for the noise and show analogous predictive performance with a model which has greater computational efficiency and is less complex to implement. We also discuss active sample selection in this framework and show through the numerical simulation of an arrested bathymetric front in an estuary, the superiority of using Mutual Information to Fisher Information, Entropy or Random sampling in terms of errors in the first two moments of the predicted distributions.


title: On the Limited Memory BFGS Method for Large Scale Optimization

abstract: We study the numerical performance of a limited memory quasi-Newton method for large scale optimization, which we call the L-BFGS method. We compare its performance with that of the method developed by Buckley and LeNir (1985), which combines cycles of BFGS steps and conjugate direction steps. Our numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence. We show that the L-BFGS method can be greatly accelerated by means of a simple scaling. We then compare the L-BFGS method with the partitioned quasi-Newton method of Griewank and Toint (1982a). The results show that, for some problems, the partitioned quasi-Newton method is clearly superior to the L-BFGS method. However we find that for other problems the L-BFGS method is very competitive due to its low iteration cost. We also study the convergence properties of the L-BFGS method, and prove global convergence on uniformly convex problems.


Inferring Recommendation Interactions in Clinical Guidelines 1 Case-studies on Multimorbidity


title: Approximately optimal continuous-time motion planning and control via Probabilistic Inference

abstract: The problem of optimal motion planing and control is fundamental in robotics. However, this problem is intractable for continuous-time stochastic systems in general and the solution is difficult to approximate if non-instantaneous nonlinear performance indices are present. In this work, we provide an efficient algorithm, PIPC (Probabilistic Inference for Planning and Control), that yields approximately optimal policies with arbitrary higher-order nonlinear performance indices. Using probabilistic inference and a Gaussian process representation of trajectories, PIPC exploits the underlying sparsity of the problem such that its complexity scales linearly in the number of nonlinear factors. We demonstrate the capabilities of our algorithm in a receding horizon setting with multiple systems in simulation.


title: Routing protocols for video surveillance drones in IEEE 802.11s Wireless Mesh Networks

abstract: In this paper we consider a video surveillance application, using a camera mounted on a drone flying over the area to be monitored and sending the video to a control center (CC). In order to ensure connectivity between the drone and the CC some relays are deployed on the ground. The resulting network is composed of a static component (relays), and a moving component (the drone). All network devices are assumed to be equipped with IEEE 802.11s air interfaces. The goal of our work is to design and validate a routing protocol appropriate for this scenario. The IEEE 802.11s standard proposes Hybrid Wireless Mesh routing Protocol (HWMP) composed of a proactive tree-based routing and the reactive Radio Metric Ad-hoc On-Demand Distance Vector (RM-AODV) scheme to support mesh networks. To address the need for reliable connectivity, faster and resource-efficient path discovery, we envisage a mixed optimized scheme, called Optimized-Hybrid Wireless Mesh Protocol (O-HWMP), where both, RM-AODV and the proactive tree-based scheme, are used at the same time. In O-HWMP the output of the tree-based routing scheme provides input to the RM-AODV, in order to reduce flooding of control packets, and to minimize delays during path discovery. Through NS3-Evalvid simulations we demonstrate that, compared to RM-AODV scheme, our proposed protocol significantly improves network performance in terms of delays, packet success rate, overhead cost, and peak-signal-to-noise-ratio metric of the received video.


title: Data Gathering Algorithms in Sensor Networks Using . . .

abstract: Gathering sensed information in an energy efficient manner is critical to operating the sensor network for a long period of time. The LEACH protocol presented by Heinzelman et al. (2000) is an elegant solution where clusters are formed to fuse data before transmitting to the base station. In this paper, we present an improved scheme, called PEGASIS (power-efficient gathering in sensor information systems), which is a near-optimal chain-based protocol that minimizes energy. In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. Simulation results show that PEGASIS performs better than LEACH. For many applications, in addition to minimizing energy, it is also important to consider the delay incurred in gathering sensed data. We capture this with the energy /spl times/ delay metric and present schemes that attempt to balance the energy and delay cost for data gathering from sensor networks. We present two new schemes to minimize energy /spl times/ delay using CDMA and non-CDMA sensor nodes. We compared the performance of direct, LEACH, and our schemes with respect to energy /spl times/ delay using extensive simulations for different network sizes. Results show that our schemes perform 80 or more times better than the direct scheme and also outperform the LEACH protocol.


title: Defeating MBA-based Obfuscation

abstract: Mixed Boolean-Arithmetic expressions are presented as a strong protection in the context of data flow obfuscation. As there is very little literature on the analysis of such obfuscated expressions, two important subjects of interest are to define what simplifying those expressions means, and how to design a simplification solution. We focus on evaluating the resilience of this technique, by giving theoretical elements to justify its efficiency and proposing a simplification algorithm using a pattern matching approach. The implementation of this solution is capable of simplifying the public examples of MBA-obfuscated expressions, demonstrating that at least a subset of MBA obfuscation lacks resilience against pattern matching analysis.


title: Crawler intelligence with machine learning and Data Mining integration

abstract: This paper presents a study conducted on intelligent crawlers used in search engines, competitive intelligence etc. Data mining algorithms identified by IEEE International Conference on Data Mining (ICDM) were used to aid machine learning and introduce intelligence into the crawler. A statistical analysis of performance of intelligent crawler is presented in this paper. Further in this paper the data mining algorithms are compared on basis of usability in crawlers.


title: Complementary Skyrmion Racetrack Memory with Voltage Manipulation

abstract: Magnetic skyrmion holds promise as information carriers in the next-generation memory and logic devices, owing to the topological stability, small size, and extremely low current needed to drive it. One of the most potential applications of skyrmion is to design racetrack memory (RM), named Sk-RM, instead of utilizing domain wall. However, current studies face some key design challenges, e.g., skyrmion manipulation, data representation, and synchronization. To address these challenges, we propose here a complementary Sk-RM structure with voltage manipulation. Functionality and performance of the proposed design are investigated with micromagnetic simulations.


title: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition 1

abstract: Functional magnetic resonance imaging (fMRI) studiesofemotion, personality, and social cognition have drawn much attention in recent years, with high-profile studies frequently reporting extremely high (e.g., >.8) correlations between brain activation and personality measures. We show that these correlations are higher than should be expected given the (evidently limited) reliability of both fMRI and personality measures. The high correlations are all the more puzzling because method sections rarely contain much detail about how the correlations were obtained. We surveyed authors of 55 articles that reported findings of this kind to determine a few details on how these correlations were computed. More than half acknowledged using a strategy that computes separate correlations for individual voxels and reports means of only those voxels exceeding chosen thresholds. We show how this nonindependent analysis inflates correlations while yielding reassuring-looking scattergrams. This analysis technique was used...


title: Effects of glans penis augmentation using hyaluronic acid gel for premature ejaculation

abstract: The main limitation of medical treatment for premature ejaculation is recurrence after withdrawal of medication. We evaluated the effect of glans penis augmentation using injectable hyaluronic acid (HA) gel for the treatment of premature ejaculation via blocking accessibility of tactile stimuli to nerve receptors. In 139 patients of premature ejaculation, dorsal neurectomy (Group I, n=25), dorsal neurectomy with glandular augmentation (Group II, n=49) and glandular augmentation (Group III, n=65) were carried out, respectively. Two branches of dorsal nerve preserving that of midline were cut at 2 cm proximal to coronal sulcus. For glandular augmentation, 2 cc of HA was injected into the glans penis, subcutaneously. At 6 months after each procedure, changes of glandular circumference were measured by tapeline in Groups II and III. In each groups, ejaculation time, patient's satisfaction and partner's satisfaction were also assessed. There was no significant difference in preoperative ejaculation time among three groups. Preoperative ejaculation times were 89.2+/-40.29, 101.54+/-59.42 and 96.5+/-52.32 s in Groups I, II and III, respectively. Postoperative ejaculation times were significantly increased to 235.6+/-58.6, 324.24+/-107.58 and 281.9+/-93.2 s in Groups I, II and III, respectively (P<0.01). The percentage of postoperative satisfaction in both patient and his partner was 68% (17/25) and 44% (7/16) in Group I, 80% (39/49) and 66% (25/38) in Group II and 75% (49/65) and 62% (32/52) in Group III, respectively. Maximal glandular girth was significantly increased from 9.16+/-0.59 to 10.95+/-0.4 cm in Group II and 8.95+/-0.54 to 11.67+/-0.71 cm in Group III, respectively. These results suggest that glandular augmentation with injectable HA gel is a safe and effective modality to reduce sensory of glans penis. Long-term follow-up for residual volume and efficacy should be requested to establish its precise therapeutic potentials in premature ejaculation.


An Enhanced Slicing Algorithm Using Nearest Distance Analysis for Layer Manufacturing


title: How the Brain Generates Movement

abstract: In this study, we assume that the brain uses a general-purpose pattern generator to transform static commands into basic movement segments. We hypothesize that this pattern generator includes an oscillator whose complete cycle generates a single movement segment. In order to demonstrate this hypothesis, we construct an oscillator-based model of movement generation. The model includes an oscillator that generates harmonic outputs whose frequency and amplitudes can be modulated by external inputs. The harmonic outputs drive a number of integrators, each activating a single muscle. The model generates muscle activation patterns composed of rectilinear and harmonic terms. We show that rectilinear and fundamental harmonic terms account for known properties of natural movements, such as the invariant bell-shaped hand velocity profile during reaching. We implement these dynamics by a neural network model and characterize the tuning properties of the neural integrator cells, the neural oscillator cells, and the inputs to the system. Finally, we propose a method to test our hypothesis that a neural oscillator is a central component in the generation of voluntary movement.


title: Network Traffic Prediction Using Radial Basis Function Neural Network Optimized by Ant Colony Algorithm

abstract: The disadvantages of the traditional radial basis function (RBF) neural network during the network traffic prediction process, such as a slow convergence rate and easy occurrence of local optima, result in low prediction precision. In this study, the ant colony optimization (ACO) algorithm is used to optimize the parameters of the RBF neural network for network traffic prediction. ACO is used to train the width and centre of the basis function of the RBF neural network, simplify the network structure, accelerate the convergence speed, prevent the occurrence of local optima, and improve the generalist ability of the RBF neural network. The experimental results show that compared with the genetic algorithm (GA)-RBF and particle swarm optimization (PSO)-RBF traffic prediction models, the proposed model exhibits higher prediction accuracy and can describe the varying trends in the network traffic well. The model used in this study exhibits strong generalization ability and good stability and therefore has practical value in network traffic prediction. Copyright © 2014 IFSA Publishing, S. L.


title: Time-resolved functional analysis of acute impairment of frataxin expression in an inducible cell model of Friedreich ataxia

abstract: Friedreich ataxia is a neurodegenerative disease caused by a GAA triplet repeat expansion in the first intron of the frataxin gene, which results in reduced expression levels of the corresponding protein. Despite numerous animal and cellular models, therapeutic options that mechanistically address impaired frataxin expression are lacking. Here, we have developed a new mammalian cell model employing the Cre/loxP recombination system to induce a homozygous or heterozygous frataxin knockout in mouse embryonic fibroblasts. Induction of Cre-mediated disruption by tamoxifen was successfully tested on RNA and protein levels. After loss of frataxin protein, cell division, aconitase activity and oxygen consumption rates were found to be decreased, while ROS production was increased in the homozygous state. By contrast, in the heterozygous state no such changes were observed. A time-resolved analysis revealed the loss of aconitase activity as an initial event after induction of complete frataxin deficiency, followed by secondarily elevated ROS production and a late increase in iron content. Initial impairments of oxygen consumption and ATP production were found to be compensated in the late state and seemed to play a minor role in Friedreich ataxia pathophysiology. In conclusion and as predicted from its proposed role in iron sulfur cluster (ISC) biosynthesis, disruption of frataxin primarily causes impaired function of ISC-containing enzymes, whereas other consequences, including elevated ROS production and iron accumulation, appear secondary. These parameters and the robustness of the newly established system may additionally be used for a time-resolved study of pharmacological candidates in a HTS manner.


title: Implementation of V/f scalar control for speed regulation of a three-phase induction motor

abstract: This paper presents the experimental implementation of V/f scalar control for a three-phase squirrel-cage induction motor. The principal goal is to control the rotation speed set by a reference and to maintain it despite the presence of disturbances. Two implementation methods were developed: Open-loop scheme, which is commonly used in industry, and closed-loop scheme, less used but it offers higher performance characteristics. MATLAB/Simulink and DS1104 controller board from dSPACE platform were used to carry out real-time experiments and final implementation.


title: Abstract Applying Genre-Based Ontologies to Enterprise Architecture

abstract: This paper elaborates the approach of using ontologies as a conceptual base for enterprise architecture (EA) descriptions. The method focuses on recognising and modelling business critical information concepts, their content, and semantics used to operate the business. Communication genres and open and semi-structured information need interviews are used as a domain analysis method. Ontologies aim to explicate the results of domain analysis and to provide a common reference model for Business Information Architecture (BIA) descriptions. The results are generalised to model further aspects of EA.


title: CoSimRank: A Flexible & Efficient Graph-Theoretic Similarity Measure

abstract: We present CoSimRank, a graph-theoretic similarity measure that is efficient because it can compute a single node similarity without having to compute the similarities of the entire graph. We present equivalent formalizations that show CoSimRank’s close relationship to Personalized PageRank and SimRank and also show how we can take advantage of fast matrix multiplication algorithms to compute CoSimRank. Another advantage of CoSimRank is that it can be flexibly extended from basic node-node similarity to several other graph-theoretic similarity measures. In an experimental evaluation on the tasks of synonym extraction and bilingual lexicon extraction, CoSimRank is faster or more accurate than previous approaches.


title: New variants of ABCA12 in harlequin ichthyosis baby

abstract: Harlequin ichthyosis (HI) is an extremely rare genetic skin disorder and the most severe form of a group of disorders, which includes lamellar ichthyosis and congenital ichthyosiform erythroderma. It consists in an autosomal recessive disorder with the majority of affected individuals being homozygous for mutation in the ABCA12 gene. This condition presents a wide range of severity and symptoms. Affected neonates often do not survive beyond the first few days of life and it was usually considered as being fatal in the past, but, with the improvement of neonatal intensive care, the survival of these patients also improved. Our report is about a harlequin baby with new variants, which have not been previously described. He presents two variants in heterozygosity in the ABCA12 gene: c.3067del (p.Tyr1023Ilefs * 22) and c.318-2A>G p(.?), inherited from the father and mother. Several aspects concerning genetics, physiopathology, diagnosis, treatment and prognosis are discussed. An intensive neonatal care and early introduction of oral retinoids improve survival rates in this kind of disorder.


title: Design of intelligent air conditioner controller using fuzzy logic

abstract: The paper presents the algorithm for intelligent air conditioner controller. This algorithm is based on fuzzy logic. The implementation is done on arduino based microcontroller with temperature and humidity as two inputs and power as output. Air conditioners based on such controller are more energy efficient as compared to conventional air conditioners. The experimental results have also shown the superiority of such controller.


title: A Recovery Method for Kinect-Like Depth Map Based on Color Image Segmentation

abstract: The original depth map captured by low-cost depth cameras such as Kinect suffers from problems of depth missing and noise, which restrict its further application. In this paper, we propose a novel recovery method of Kinect-like depth map utilizing the corresponding color image. The proposed method consists of two steps: hole-filling by image in painting and denoising by fast guided filter. Experiments show that our method gives satisfactory result.


IoT-LAB tutorial : hands-on practice with a very large scale testbed tool for the Internet of Things


title: Reducing the bandwidth of sparse symmetric matrices

abstract: The finite element displacement method of analyzing structures involves the solution of large systems of linear algebraic equations with sparse, structured, symmetric coefficient matrices. There is a direct correspondence between the structure of the coefficient matrix, called the stiffness matrix in this case, and the structure of the spatial network delineating the element layout. For the efficient solution of these systems of equations, it is desirable to have an automatic nodal numbering (or renumbering) scheme to ensure that the corresponding coefficient matrix will have a narrow bandwidth. This is the problem considered by R. Rosen 1 . A direct method of obtaining such a numbering scheme is presented. In addition several methods are reviewed and compared.


title: Transformation of African Agrifood Systems in the New Era of Rapid Urbanization and the Emergence of a Middle Class

abstract: It is common knowledge that rural Africa faces formidable problems such as poverty and malnutrition, inadequate farm yields, low use of fertilizers, certified seed and irrigation, and often poor infrastructure. We acknowledge those problems. But in this paper we present the message that there is also good news for African food systems, in this new era of urbanization and emergence of a middle class, that are emerging and developing firms all along the supply chain: the emergence of a “Quiet Revolution in African food supply chains,” led mainly by African entrepreneurs in tens of thousands of small enterprises, scores, and perhaps soon hundreds, of medium and large-scale firms like Bakhresa grain millers in Tanzania, Shoprite and Uchumi supermarkets chains in South Africa and Kenya, Zartech chicken processors in Nigeria, and so on. We show in this paper that African food markets have expanded 6-8-fold over the past four decades with most of that growth in the past two decades, and have begun a transformation. Haggblade (2011) presents a projection that the African food market will grow another 6-fold in the next four decades. That means that the African food market will have expanded more than a dozen-fold in a human lifespan, a massive achievement. Africa’s urban areas have grown quickly, and now constitute half or more of overall food consumption. Food consumption itself is changing rapidly, with a shift beyond grains into non-grain foods like dairy, fish, meat, vegetables, fruit, and tubers, and heavily into processed foods. A substantial middle class has emerged, and is an important driver of food system change, but is not the only driver of change, as we also show that the poor’s food consumption patterns have changed deeply. A central implication of our paper is the need for good public policy and investments to leverage urbanization and diet diversification, to develop food supply chains, to feed millions of people in growing cities millions of rural household food buyers, and to increase incomes of poor rural households who produce food and are employed in food supply chain activities. Rural suppliers need to sell to sources of dynamic, growing demand such as domestic urban


title: Wavelet based image fusion techniques

abstract: The fusion of images is the process of combining two or more images into a single image retaining important features from each. Fusion is an important technique within many disparate fields such as remote sensing, robotics and medical applications. The result of image fusion is a single image which is more suitable for human and machine perception or further image-processing tasks. The image fusion algorithm based on wavelet transform is proposed to prove the geometric resolution of the images, in which two images to be processed are firstly decomposed into sub images and then the infor-mation is performed using these images under the certain criteria and finally these sub images are reconstructed into result image with plentiful information.. In this paper four different image fusion me-thods based wavelet transform are implemented. And the results are compared and best method is found.


title: Electromagnetic and Mechanical design of a Fractional-slot-windings Axial-flux PM synchronous machine with Soft Magnetic Compound Stator

abstract: Despite their favorable aspect ratio, Axial Flux PM Synchronous machines can compete with traditional radial flux machines in terms of torque and power density only if the number of pole-pairs is sufficiently high and the ratio axial length/outer diameter is low. Since Axial Flux PM Synchronous machines have an inner stator hole, the number of stator slots is bounded, therefore in order to increase the number of poles per slots fractional slot windings arrangements can be utilized. Frequently, this procedure results into a more compact winding design, resulting in lower copper losses due to shorter end connections. This paper deals with the electromagnetic and mechanical design of a fractional windings Axial flux PM synchronous machine with soft magnetic composite featuring low eddy-currents losses at high frequency and self-cooling air channel system. The whole design procedure was done using Finite Element Analysis and multiphysics tools.


title: RDRPOSTagger: A Ripple Down Rules-based Part-Of-Speech Tagger

abstract: This paper describes our robust, easyto-use and language independent toolkit namely RDRPOSTagger which employs an error-driven approach to automatically construct a Single Classification Ripple Down Rules tree of transformation rules for POS tagging task. During the demonstration session, we will run the tagger on data sets in 15 different languages.


title: Pedestrian detection procedure integrated into an 24 GHz automotive radar

abstract: Driving a car is a dangerous task! There are about 5000 fatalities on German streets every year, which is absolutely too much. Human beings have strong limitations in the ability to measure precisely the distance and the speed difference between cars which is the reason for several accidents. Therefore some individual assistance is needed. A radar sensor measures the target range and radial velocity very accurate and with high resolution even in multiple target situations and there is tremendous progress in 24 GHz radar sensor development. The objective of this paper is to protect vulnerable road users by a target recognition scheme. Therefore, a pedestrian detection procedure is additionally integrated into the 24 GHz automotive radar sensor.


title: Starting Performance Improvement of Line-Start Permanent-Magnet Synchronous Motor Using Composite Solid Rotor

abstract: This paper presents a composite solid rotor designed to improve starting performance of the line-start permanent-magnet synchronous motor (LSPMSM). The rotor is mainly constituted by the solid steel which can inherently generate high starting torque. Moreover, the axial slots milled on the rotor surface deepen the flux penetration into core and a brass made cage attached to the rotor enlarges the eddy current density. With above promotions, the induction torque increases, and hence drives the motor to startup and synchronize under heavy load conditions. The enhanced starting capability by applying the proposed rotor for an LSPMSM is validated by the 2-D time-stepping finite-element method (FEM). To guarantee a well match with the 3-D construction where end effects occur, an equivalent circuit is built in the 2-D method to represent the uneven ring fractions. Simultaneously, considering the non-axial currents in core ends, conductivity of the solid-rotor segments in 2-D field is corrected by a multistep optimum-seeking technique. Calculation precision of the 2-D FEM covering end effects is certificated by the corresponding 3-D FEM with good results agreement.


title: Reconstruction of acquired oromandibular defects.

abstract: Familiarity with mandibular and cervical anatomy is crucial in achieving mandibular reconstruction. The surgeon must evaluate which components of the hard and soft tissue are missing in selecting a method of reconstruction. Complexity of mandibular reconstruction ranges from simple rigid internal fixation to microvascular free tissue transfer, depending on factors related to both defect and patient. Modern techniques for microvascular tissue transfer provide a wide array of reconstructive options that can be tailored to patients’ specific needs.


title: Novel strategies in the oral delivery of antidiabetic peptide drugs – Insulin, GLP 1 and its analogs

abstract: Abstract As diabetes is a complex disorder being a major cause of mortality and morbidity in epidemic rates , continuous research has been done on new drug types and administration routes. Up to now, a large number of therapeutic peptides have been produced to treat diabetes including insulin, glucagon-like peptide-1 (GLP-1) and its analogs. The most common route of administration of these antidiabetic peptides is parenteral. Due to several drawbacks associated with this invasive route, delivery of these antidiabetic peptides by the oral route has been a goal of pharmaceutical technology for many decades. Dosage form development should focus on overcoming the limitations facing oral peptides delivery as degradation by proteolytic enzymes and poor absorption in the gastrointestinal tract (GIT). This review focuses on currently developed strategies to improve oral bioavailability of these peptide based drugs; evaluating their advantages and limitations in addition to discussing future perspectives on oral peptides delivery. Depending on the previous reports and papers, the area of nanocarriers systems including polymeric nanoparticles, solid lipid nanoparticles, liposomes and micelles seem to be the most promising strategy that could be applied for successful oral peptides delivery; but still further potential attempts are required to be able to achieve the FDA approved oral antidiabetic peptide delivery system.


title: Transfer Impedance Measurement of Shielded Cables Through Localized Injection

abstract: This letter presents a method to determine experimentally the transfer impedance of electrically short shielded cables up to 100 or 150 MHz (depending on the cable length). The proposed method aims to create a disturbance along the cable shield with a localized injection device as for instance a toroidal magnetic probe. Two different experimental setups referred as “scalar” and “vectorial” setups are proposed. The validations are performed on cables of known transfer impedance with respect to other experimental techniques and theoretical formulations. The presented results exhibit the advantages and drawbacks of both setups with respect to the existing experimental methods.


title: Metaheuristic algorithms comparison for the LC - Voltage controlled oscillators optimal design

abstract: The goal of this paper is to present a comparison among three known metaheuristics: Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA). For the comparison, the design of an LC - Voltage Controlled Oscillator (LC-VCO) is considered, where the minimization of both VCO phase noise and power consumption is envisaged. The objective of this comparison is to find the algorithm yielding the best solution. The validity of the solution obtained with each metaheuristic algorithm is checked against HSPICE/RF simulation results. Robustness checks for each algorithm are presented at the end of this paper.


title: TabVec: Table Vectors for Classification of Web Tables

abstract: There are hundreds of millions of tables in Web pages that contain useful information for many applications. Leveraging data within these tables is difficult because of the wide variety of structures, formats and data encoded in these tables. TabVec is an unsupervised method to embed tables into a vector space to support classification of tables into categories (entity, relational, matrix, list, and non-data) with minimal user intervention. TabVec deploys syntax and semantics of table cells, and embeds the structure of tables in a table vector space. This enables superior classification of tables even in the absence of domain annotations. Our evaluations in four real world domains show that TabVec improves classification accuracy by more than 20% compared to three state of the art systems, and that those systems require significant in domain training to achieve good results.


title: When a city tells a story: urban topic analysis

abstract: This paper explores the use of textual and event-based citizen-generated data from services such as Twitter and Foursquare to study urban dynamics. It applies a probabilistic topic model to obtain a decomposition of the stream of digital traces into a set of urban topics related to various activities of the citizens in the course of a week. Due to the combined use of implicit textual and movement data, we obtain semantically rich modalities of the urban dynamics and overcome the drawbacks of several previous attempts. Other important advantages of our method include its flexibility and robustness with respect to the varying quality and volume of the incoming data. We describe an implementation architecture of the system, the main outputs of the analysis, and the derived exploratory visualisations. Finally, we discuss the implications of our methodology for enriching location-based services with real-time context.


title: RAPID: Rating Pictorial Aesthetics using Deep Learning

abstract: Effective visual features are essential for computational aesthetic quality rating systems. Existing methods used machine learning and statistical modeling techniques on handcrafted features or generic image descriptors. A recently-published large-scale dataset, the AVA dataset, has further empowered machine learning based approaches. We present the RAPID (RAting PIctorial aesthetics using Deep learning) system, which adopts a novel deep neural network approach to enable automatic feature learning. The central idea is to incorporate heterogeneous inputs generated from the image, which include a global view and a local view, and to unify the feature learning and classifier training using a double-column deep convolutional neural network. In addition, we utilize the style attributes of images to help improve the aesthetic quality categorization accuracy. Experimental results show that our approach significantly outperforms the state of the art on the AVA dataset.


title: Meta-path based multi-network collective link prediction

abstract: Online social networks offering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simultaneously to enjoy specific services provided by different networks. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) prediction problem. In multiple partially aligned social networks, users can be extensively correlated with each other by various connections. To categorize these diverse connections among users, 7 "intra-network social meta paths" and 4 categories of "inter-network social meta paths" are proposed in this paper. These "social meta paths" can cover a wide variety of connection information in the network, some of which can be helpful for solving the multi-network link prediction problem but some can be not. To utilize useful connection, a subset of the most informative "social meta paths" are picked, the process of which is formally defined as "social meta path selection" in this paper. An effective general link formation prediction framework, Mli (Multi-network Link Identifier), is proposed in this paper to solve the multi-network link (formation) prediction problem. Built with heterogenous topological features extracted based on the selected "social meta paths" in the multiple partially aligned social networks, Mli can help refine and disambiguate the prediction results reciprocally in all aligned networks. Extensive experiments conducted on real-world partially aligned heterogeneous networks, Foursquare and Twitter, demonstrate that Mli can solve the multi-network link prediction problem very well.


title: Lymphomas and bone tumors: clinical presentation, management, and potential late effects of current treatment strategies.

abstract: While many pediatric malignancies are seen predominantly in pre-school children, many cases of childhood non-Hodgkin's lymphoma and most cases of Hodgkin's disease and bone tumors are seen in the older child and adolescent. This review focuses on current knowledge concerning the epidemiology, histopathology, molecular biology, clinical presentation, diagnosis, staging, treatment, and prognosis for older children and adolescents diagnosed with lymphoma or either of the two commonly seen childhood bone tumors, namely osteosarcoma and Ewing's sarcoma. Survival figures for all of these childhood malignancies have increased markedly in the past two decades. We now have the relatively new experience of having an increasingly large population of childhood cancer survivors to study and, unfortunately, are beginning to see the long-term consequences of these more successful treatments. This review concludes with an overview of the potential late effects of cancer therapy, effects that may first be detected by the primary care physician caring for the adolescent who is a cancer survivor.


title: Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics

abstract: We consider methods for learning vector representations of SQL queries to support generalized workload analytics tasks, including workload summarization for index selection and predicting queries that will trigger memory errors. We consider vector representations of both raw SQL text and optimized query plans, and evaluate these methods on synthetic and real SQL workloads. We find that general algorithms based on vector representations can outperform existing approaches that rely on specialized features. For index recommendation, we cluster the vector representations to compress large workloads with no loss in performance from the recommended index. For error prediction, we train a classifier over learned vectors that can automatically relate subtle syntactic patterns with specific errors raised during query execution. Surprisingly, we also find that these methods enable transfer learning, where a model trained on one SQL corpus can be applied to an unrelated corpus and still enable good performance. We find that these general approaches, when trained on a large corpus of SQL queries, provides a robust foundation for a variety of workload analysis tasks and database features, without requiring application-specific feature engineering.


title: Self-awareness and transgression in children: Two field studies.

abstract: Two field studies explored the relationship between self-awareness and transgressive behavior. In the first study, 363 Halloween trick-or-treaters were instructed to only take one candy. Self-awareness induced by the presence of a mirror placed behind the candy bowl decreased transgression rates for children who had been individuated by asking them their name and address, but did not affect the behavior of children left anonymous. Self-awareness influenced older but not younger children. Naturally occurring standards instituted by the behavior of the first child to approach the candy bowl in each group were shown to interact with the experimenter's verbally stated standard. The behavior of 349 subjects in the second study replicated the findings in the first study. Additionally, when no standard was stated by the experimenter, children took more candy when not self-aware than when self-aware.


title: The UNET-2 modem — An extensible tool for underwater networking research

abstract: Several decades of research in underwater communication and networking has resulted in novel and innovative solutions to combat challenges such as long delay spread, rapid channel variation, significant Doppler, high levels of non-Gaussian noise, limited bandwidth and long propagation delays. Many of the physical layer solutions can be tested by transmitting carefully designed signals, recording them after passing through the underwater channel, and then processing them offline using appropriate algorithms. However some solutions requiring online feedback to the transmitter cannot be tested without real-time processing capability in the field. Protocols and algorithms for underwater networking also require real-time communication capability for experimental testing. Although many modems are commercially available, they provide limited flexibility in physical layer signaling and sensing. They also provide limited control over the exact timing of transmission and reception, which can be critical for efficient implementation of some networking protocols with strict time constraints. To aid in our physical and higher layer research, we developed the UNET-2 software-defined modem with flexibility and extensibility as primary design objectives. We present the hardware and software architecture of the modem, focusing on the flexibility and adaptability that it provides researchers with. We describe the network stack that the modem uses, and show how it can also be used as a powerful tool for underwater network simulation. We illustrate the flexibility provided by the modem through a number of practical examples and experiments.


title: BLOOGO: BLOOm filter based GOssip algorithm for wireless NDN

abstract: Modern ad-hoc networks focus on data rather than ob- solete end-to-end communications. This paper describes BlooGo, a gossip algorithm that disseminates messages throughout the network with a minimum number of trans- missions. Its peculiarity lies on the fact that without any knowledge of the network the receiver of a packet can de- cides autonomously whether to forward it or not. All the information that BlooGo uses to decide the utility of a transmission is enclosed in the packet as a bloom filter; this way the parties, i.e. sender and receiver, never have to communicate directly. This approach makes BlooGo state-less; lightweight; power-efficient; ideal for sensors or embedded devices that use the NDN philosophy.


title: Ensemble-style Self-training on Citation Classification

abstract: Classification of citations into categories such as use, refutation, comparison etc. may have several relevant applications for digital libraries such as paper browsing aids, reading recommendations, qualified citation indexing, or fine-grained impact factor calculation. Most citation classification approaches described so far heavily rely on rule systems and patterns tailored to specific science domains. We focus on a less manual approach by learning domaininsensitive features from textual, physical, and syntactic aspects. Our experiments show the effectiveness of this feature set with various machine learning algorithms on datasets of different sizes. Furthermore, we build an ensemble-style selftraining classification model and get better classification performance using only few training data, which largely reduces the manual annotation work in this task.


title: 3D Sketching in AutoSketch

abstract: The paper presents a novel method to sketch in 3D for automotive styling based on a computer-aided styling system — AutoSketch, which has been prototyped by using ThinkDesign API. Differing from the traditional CAS/CAD systems, AutoSketch uses a digital pen to draw/sketch in a natural way on digital tablet/screen to create 3D surface models. The method demonstrates how spatial curves/surfaces have been created. Planar points are converted into spatial points, planar strokes into spatial curves on working plane or surfaces through the projection and intersection, and spatial curves into surfaces in hand sketching ways.


title: Intensity Rank Estimation of Facial Expressions Based on a Single Image

abstract: In this paper, we propose a framework that estimates the discrete intensity rank of a facial expression based on a single image. For most people, judging whether an expression is more intense than others is easier than determining its real-valued intensity degree, and hence the relative order of two expressions is more distinguishable than the exact difference between them. We utilize the relative order to construct an image-based ranking approach for inferring the discrete ranks. The challenge of image-based approaches is to conduct a representation for subtle expression changes. We employ an efficient descriptor, scattering transform, which is translation invariant and can linearize deformations. This scattering representation recovers the lost high frequencies and retains discrimination under invariant property. Our experimental results demonstrate that the proposed framework with scattering transform outperforms other compared feature descriptors and algorithms.


Cuckoo Optimization Algorithm based Image Enhancement


title: A theory of attention: Variations in the associability of stimuli with reinforcement.

abstract: According to theories of selective attention, learning about a stimulus depends on attending to that stimulus; this is represented in two-stage models by saying that subjects switch in analyzers as well as learning stimulusresponse associations. This assumption, however, is equally well represented in a formal model by the incorporation of a stimulus-specific learning-rate parameter, a, into the equations describing changes in the associative strength of stimuli. Theories of selective attention have also assumed (a) that subjects learn to attend to and ignore relevant and irrelevant stimuli (i.e., that a may increase or decrease depending on the correlation of a stimulus with reinforcement) and (b) that there is an inverse relationship between the probabilities of attending to different stimuli (i.e., that an increase in a to one stimulus is accompanied by a decrease in a to others). The first assumption is used to explain the phenomena of acquired distinctiveness and dimensional transfer, the second those of overshadowing and blocking. Although the first assumption is justified by the data, the second is not: Overshadowing and blocking are better explained by the choice of an appropriate rule for changing a, such that a decreases to stimuli that signal no change from the probability of reinforcement predicted by other stimuli.


title: Abstract Fast and robust text detection in images and video frames

abstract: Text in images and video frames carries important information for visual content understanding and retrieval. In this paper, by using multiscale wavelet features, we propose a novel coarse-to-fine algorithm that is able to locate text lines even under complex background. First, in the coarse detection, after the wavelet energy feature is calculated to locate all possible text pixels, a density-based region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by structural information. Secondly, in the fine detection, with four kinds of texture features extracted to represent the texture pattern of a text line, a forward search algorithm is applied to select the most effective features. Finally, an SVM classifier is used to identify true text from the candidates based on the selected features. Experimental results show that this approach can fast and robustly detect text lines under various conditions.


title: iSports: A web-oriented expert system for talent identification in soccer

abstract: Abstract Nowadays soccer is the most practiced sport in the world and moves a multimillionaire market. Therefore, a club that is able to recruit and develop talented players to theirs fullest potential has a lot of advantages and economic benefits. However, in most clubs the players are selected through scouts and coaches recommendation, with predictive success based mostly on intuition than other objective criteria. In addition, it is known that talent development and identification is a multifactorial process involving many characteristics. To this end, this paper proposes the creation of performance indicators based on multivariate statistical analysis. Usual principal components and factor analysis are performed to construct physical, technical and general score and copula modeling is proposed to create the consistency index, which generalizes the Z score method. With these indicators, a web-oriented expert system for analyzing sport data in real time via R software is proposed as a powerful tool for talent identification in soccer. This system, the so called iSports, allows the monitoring and continuous comparison of athletes in a simple and efficient way, taking into account essentials aspects, as well as identifying candidate talented that have above the average performance, that is, who stand out from the studied population of soccer players. In order to promote and popularize the access of information and the statistical science applied in the sports context, the iSports system can be used in any training center of the country, impacting the increase of knowledge of the athletes in training phase at any school, city or region.


title: Learning Discriminative Representations from RGB-D Video Data

abstract: Recently, the low-cost Microsoft Kinect sensor, which can capture real-time high-resolution RGB and depth visual information, has attracted increasing attentions for a wide range of applications in computer vision. Existing techniques extract hand-tuned features from the RGB and the depth data separately and heuristically fuse them, which would not fully exploit the complementarity of both data sources. In this paper, we introduce an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGB-D video data for visual recognition tasks. We address this as an optimization problem using our proposed restricted graph-based genetic programming (RGGP) approach, in which a group of primitive 3D operators are first randomly assembled as graph-based combinations and then evolved generation by generation by evaluating on a set of RGB-D video samples. Finally the best-performed combination is selected as the (near-)optimal representation for a pre-defined task. ::: ::: The proposed method is systematically evaluated on a new hand gesture dataset, SKIG, that we collected ourselves and the public MSR Daily Activity 3D dataset, respectively. Extensive experimental results show that our approach leads to significant advantages compared with state-of-the-art hand-crafted and machine-learned features.


title: SMT-based Task- and Network-level Static Schedule Generation for Time-Triggered Networked Systems

abstract: In Ethernet-based time-triggered networks, like TTEthernet, a global communication scheme, for which the schedule synthesis is known to be an NP-complete problem, establishes contention-free windows for the exchange of messages with guaranteed low latency and minimal jitter. However, in order to achieve end-to-end determinism at the application level, software tasks running on the end-system nodes need to obey a similar execution scheme with tight dependencies towards the network domain. In this paper we address the simultaneous co-synthesis of network as well as application schedules for preemptive time-triggered tasks communicating in a switched multi-speed time-triggered network. We use Satisfiability Modulo Theories (SMT) to formulate the scheduling constraints and solve the resulting problem using a state-of-the-art SMT solver. Furthermore, we introduce a novel incremental scheduling approach, based on the demand bound test for asynchronous constrained-deadline periodic tasks, which significantly improves scalability for the average case without sacrificing schedulability. We demonstrate the performance of our approach using synthetic network topologies and system configurations.


title: A MIMO H-shape Dielectric Resonator Antenna for 4G Applications

abstract: In this article, a Multiple-Input-Multiple-Output (MIMO) H-shape Dielectric Resonator Antenna (DRA) is designed and simulated at 2.6 GHz for 4G applications. The proposed structure consists of H-shape DRA ( =10) which is mounted on FR4 substrate ( =4.6), and feed by two different feeding mechanisms. First, microstrip with slot coupling as Port 1. Second, coaxial probe as Port 2. The electrical properties of the proposed MIMO H-shape DRA in term of return loss, bandwidth and gain are completely obtained by using CST Microwave Studio Suite Software. The simulated results demonstrated a return loss more than 20 dB, an impedance bandwidth of 26 % (2.2 – 2.9 GHz), and gain of 6.11 dBi at Port 1. Then, a return loss more than 20 dB, an impedance bandwidth of 13 % (2.2 – 2.7 GHz), and gain of 6.63 dBi at Port 2. Both ports indicated impedance bandwidth more than 10 %, return loss lower than 20 dB, and gain more than 10 dBi at 2.6 GHz. The simulated electrical properties of the proposed design show a good potential for LTE applications.


title: An algorithm to increase speech intelligibility for hearing-impaired listeners in novel segments of the same noise type

abstract: Machine learning algorithms to segregate speech from background noise hold considerable promise for alleviating limitations associated with hearing impairment. One of the most important considerations for implementing these algorithms into devices such as hearing aids and cochlear implants involves their ability to generalize to conditions not employed during the training stage. A major challenge involves the generalization to novel noise segments. In the current study, sentences were segregated from multi-talker babble and from cafeteria noise using an algorithm that employs deep neural networks to estimate the ideal ratio mask. Importantly, the algorithm was trained on segments of noise and tested using entirely novel segments of the same nonstationary noise type. Substantial sentence-intelligibility benefit was observed for hearing-impaired listeners in both noise types, despite the use of unseen noise segments during the test stage. Interestingly, normal-hearing listeners displayed benefit in babble but not in cafeteria noise. This result highlights the importance of evaluating these algorithms not only in human subjects, but in members of the actual target population.


Bridging the gap between computable and expressive event representations in Social Media


title: Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

abstract: Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments.


title: A Survey on Security for Mobile Devices

abstract: Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research field is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach.


title: Renewable and sustainable approaches for desalination

abstract: Freshwater and energy are essential commodities for well being of mankind. Due to increasing population growth on the one hand, and rapid industrialization on the other, today's world is facing unprecedented challenge of meeting the current needs for these two commodities as well as ensuring the needs of future generations. One approach to this global crisis of water and energy supply is to utilize renewable energy sources to produce freshwater from impaired water sources by desalination. Sustainable practices and innovative desalination technologies for water reuse and energy recovery (staging, waste heat utilization, hybridization) have the potential to reduce the stress on the existing water and energy sources with a minimal impact to the environment. This paper discusses existing and emerging desalination technologies and possible combinations of renewable energy sources to drive them and associated desalination costs. It is suggested that a holistic approach of coupling renewable energy sources with technologies for recovery, reuse, and recycle of both energy and water can be a sustainable and environment friendly approach to meet the world's energy and water needs. High capital costs for renewable energy sources for small-scale applications suggest that a hybrid energy source comprising both grid-powered energy and renewable energy will reduce the desalination costs considering present economics of energy.


title: Attention, Distraction, and Cognitive Control Under Load

abstract: The extent to which people can focus attention in the face of irrelevant distractions has been shown to critically depend on the level and type of information load involved in their current task. The ability to focus attention improves under task conditions of high perceptual load but deteriorates under conditions of high load on cognitive control processes such as working memory. I review recent research on the effects of load on visual awareness and brain activity, including changing effects over the life span, and I outline the consequences for distraction and inattention in daily life and in clinical populations.


title: Inactivation of Bacillus subtilis spores with ozone and monochloramine

abstract: The inactivation kinetics of Bacillus subtilis spores with ozone and monochloramine was characterized by a lag phase followed by a pseudo-first-order rate of inactivation. The lag phase decreased and the post-lag phase rate constant increased with increasing temperature within the range investigated (1-30 degrees C for ozone, 1-20 degrees C for monochloramine). The corresponding activation energies were 46820 J/mol for ozone and 79640 J/mol for monochloramine. The CT concept was found to be valid within the concentration range investigated of 0.44-4.8 mg/l for ozone, and 3.8-7.7 mg/l as Cl(2) for monochloramine. The inactivation kinetics of B. subtilis spores with both ozone and monochloramine varied with pH within the range of pH 6-10 investigated. The fastest ozone and monochloramine inactivation rates were observed at pH 10 and 6, respectively. Different stocks of the same strain of B. subtilis spores had different resistance to ozone and monochloramine mainly because of discrepancies in the extent of the lag phase. B. subtilis spores might not be conservative surrogates for C. parvum oocysts for ozone disinfection at relatively low temperature mainly due to the spores having a lower activation energy compared to that for the oocysts. In contrast, the activation energy for monochloramine was comparable for both microorganisms but differences in the extent of the lag phase might result in the spores being overly conservative surrogates for the oocysts at relatively low temperature.


title: Binary compatible graphics support in Android for running iOS apps

abstract: Mobile apps make extensive use of GPUs on smartphones and tablets to access Web content. To support pervasive Web content, we introduce three key OS techniques for binary graphics compatibility necessary to build a real-world system to run iOS and Android apps together on the same smartphone or tablet. First diplomat usage patterns manage resources to bridge proprietary iOS and Android graphics implementations. Second, thread impersonation allows a single thread-specific context to be shared amongst multiple threads using multiple iOS and Android personas. Third, dynamic library replication allows multiple, independent instances of the same library to be loaded in a single process to support iOS apps on Android while using multiple graphics API versions at the same time. We use these techniques to build a system prototype, and demonstrate that it runs widely-used iOS apps, including apps such as Safari that use the popular GPU-accelerated WebKit framework, using a Google Nexus tablet running Android.


title: Prepubertal hidradenitis suppurativa successfully treated with botulinum toxin A.

abstract: Hidradenitis suppurativa (HS) is a recurrent scarring suppurative disease of the apocrine gland-bearing areas of the body that usually affects the intertriginous areas. Prepubertal HS is a rare condition; fewer than 2% of patients have onset of the disease before the age of 11. Management of HS, in children and adults, may be difficult because of its chronic nature and lack of response to standard therapeutic options, and there is little evidence of the efficacy of the different treatments in randomized clinical trials or after long-term followup. Among all therapies, botulinum toxin A (BTX) has been regarded as effective for isolated case reports of adult-onset HS. Recently, we succeeded in treating a case of prepubertal HS with BTX, which to our knowledge has not previously been described.


title: Target detection using GPS signals of opportunity

abstract: We investigate the capability of GPS signals of opportunity to detect and localize targets on the sea surface. The proposed approach to target detection is new, and stems from the advantages offered by GPS-Reflectometry (GPS-R) in terms of spatial and temporal sampling, and low cost/low power technology, extending the range of applications of GPS-R beyond remote sensing. Here the exploitation of GPS signals backscattered from a target is proposed, to enhance the target return with respect to the sea clutter. A link budget is presented, showing that the target return is stronger than the background sea clutter when certain conditions are verified. The findings agree with the only empirical measurement found in literature, where a strong return from a target was fortuitously registered during an airborne campaign. This study provides a first proof-of-concept of GPS-based target detection, highlighting all the potentials of this innovative approach.


title: From stress to inflammation and major depressive disorder: a social signal transduction theory of depression.

abstract: Major life stressors, especially those involving interpersonal stress and social rejection, are among the strongest proximal risk factors for depression. In this review, we propose a biologically plausible, multilevel theory that describes neural, physiologic, molecular, and genomic mechanisms that link experiences of social-environmental stress with internal biological processes that drive depression pathogenesis. Central to this social signal transduction theory of depression is the hypothesis that experiences of social threat and adversity up-regulate components of the immune system involved in inflammation. The key mediators of this response, called proinflammatory cytokines, can in turn elicit profound changes in behavior, which include the initiation of depressive symptoms such as sad mood, anhedonia, fatigue, psychomotor retardation, and social-behavioral withdrawal. This highly conserved biological response to adversity is critical for survival during times of actual physical threat or injury. However, this response can also be activated by modern-day social, symbolic, or imagined threats, leading to an increasingly proinflammatory phenotype that may be a key phenomenon driving depression pathogenesis and recurrence, as well as the overlap of depression with several somatic conditions including asthma, rheumatoid arthritis, chronic pain, metabolic syndrome, cardiovascular disease, obesity, and neurodegeneration. Insights from this theory may thus shed light on several important questions including how depression develops, why it frequently recurs, why it is strongly predicted by early life stress, and why it often co-occurs with symptoms of anxiety and with certain physical disease conditions. This work may also suggest new opportunities for preventing and treating depression by targeting inflammation.


title: IaaS Cloud Architecture: From Virtualized Datacenters to Federated Cloud Infrastructures

abstract: As a key component in a modern datacenter, the cloud operating system is responsible for managing the physical and virtual infrastructure, orchestrating and commanding service provisioning and deployment, and providing federation capabilities for accessing and deploying virtual resources in remote cloud infrastructures.


title: Motor rehabilitation using virtual reality

abstract: Virtual Reality (VR) provides a unique medium suited to the achievement of several requirements for effective rehabilitation intervention. Specifically, therapy can be provided within a functional, purposeful and motivating context. Many VR applications present opportunities for individuals to participate in experiences, which are engaging and rewarding. In addition to the value of the rehabilitation experience for the user, both therapists and users benefit from the ability to readily grade and document the therapeutic intervention using various systems. In VR, advanced technologies are used to produce simulated, interactive and multi-dimensional environments. Visual interfaces including desktop monitors and head-mounted displays (HMDs), haptic interfaces, and real-time motion tracking devices are used to create environments allowing users to interact with images and virtual objects in real-time through multiple sensory modalities. Opportunities for object manipulation and body movement through virtual space provide frameworks that, in varying degrees, are perceived as comparable to similar opportunities in the real world. This paper reviews current work on motor rehabilitation using virtual environments and virtual reality and where possible, compares outcomes with those achieved in real-world applications.


A Novel Continuum Trunk Robot Based on Contractor Muscles


title: A novel dual-polarized broadband UHF blade antenna for avionic applications

abstract: In this contribution, a novel dual polarized broadband passive antenna, aimed at Direction Finding (DF) applications in the UHF spectrum, is presented. Preliminary results, obtained by full wave simulations, confirm the stability of the antenna radiation pattern over the whole working frequency band, thus resulting an excellent candidate as electromagnetic sensor for dual polarized DF systems.


title: The Relation Between Implicit and Explicit Learning: Evidence for Parallel Development

abstract: Much research has focused on the separability of implicit and explicit learning, but less has focused on how they might interact. A recent model suggests that in the motor-skill domain, explicit knowledge can guide movement, and the implicit system learns in parallel, based on these movements. Functional imaging studies do not support that contention, however; they indicate that learning is exclusively implicit or explicit. In the experiment reported here, participants learned a motor sequencing task either implicitly or explicitly. At transfer, most of the stimuli were random, but the sequence occasionally appeared; thus, it was not obvious that explicit knowledge could be applied to the task. Nevertheless, participants with explicit training showed sequence knowledge equivalent to those with implicit training, implying that implicit knowledge had been acquired in parallel with explicit knowledge. This result has implications for the development of automaticity and of motor-skill learning.


title: Trigonella seed extract ameliorates inflammation via regulation of the inflammasome adaptor protein ASC

abstract: Trigonella foenum-graecum (fenugreek) is an important medicinal plant, well known for its anti-inflammatory properties. However, the underlying cellular and molecular mechanisms of its action remain largely unknown. The apoptosis associated speck like protein containing a caspase recruitment domain (CARD) (ASC) is central to inflammatory and cell death pathways in innate and adaptive immunity. Here, we show that fenugreek seed extract provides cytoprotection to bacterial lipopolysaccharide (LPS) inflammed and nanosilica-treated fibroblasts via a reactive oxygen species independent pathway. All atom molecular dynamics simulations of ASC-ligand complex reveal that individual phytochemicals in fenugreek can bind to ASC via specific non-covalent interactions. These data show that a synergistic effect of fenugreek phytochemicals with the ASC protein alters its molecular properties resulting in altered cellular function. Such information is crucial to the development of targeted therapeutic interventions for inflammatory diseases.


title: A Statistical-Relational Activity Recognition Framework for Ambient Assisted Living Systems

abstract: Smart environments with ubiquitous sensing technologies are a promising perspective for reliable and continuous healthcare systems with reduced costs. A primary challenge for such assisted living systems is the automated recognition of everyday activities carried out by humans in their own home. In this work, we investigate the use of Markov Logic Networks as a framework for activity recognition within intelligent home-like environments equippedwith pervasive light-weight sensor technologies. In particular, we explore the ability of MLNs to capture temporal relations and background knowledge for improving the recognition performance.


title: Three Misconceptions About Age and L2 Learning

abstract: Age has often been considered a major, if not the primary, factor determining success in learning a second or foreign language. Children are generally considered capable of acquiring a new language rapidly and with little effort, whereas adults are believed to be doomed to failure. Although older learners are indeed less likely than young children to master an L2, a close examination of studies relating age to language acquisition reveals that age differences reflect differences in the situation of learning rather than in capacity to learn. They do not demonstrate any constraint on the possibility that adults can become highly proficient, even nativelike, speakers of L2s. Researchers, in other words, have often committed the same blunders as members of the general public: misinterpretation of the facts relating to speed of acquisition, misattribution of age differences in language abilities to neurobiological factors, and, most notably, a misemphasis on poor adult learners and an underemphasis on adults who master L2s to nativelike levels. By clarifying these misconceptions, we hope this article will lead to a better understanding of L2 learning and, in turn, better approaches to L2 teaching.


title: Limitations of concurrency in transaction processing

abstract: Given the pairwise probability of conflict p among transactions in a transaction processing system, together with the total number of concurrent transactions n, the effective level of concurrency E(n,p) is defined as the expected number of the n transactions that can run concurrently and actually do useful work. Using a random graph model of concurrency, we show for three general classes of concurrency control methods, examples of which are (1) standard locking, (2) strict priority scheduling, and (3) optimistic methods, that (1) E(n, p) l n(1 - p/2) n-1 , (2) E(n, p) l (1 - (1 - p) n )/p, and (3) 1 + ((1 - p)/p)ln(p(n - 1) + 1) l E(n, p) l 1 + (1/p)ln(p(n - 1) + 1). Thus, for fixed p, as n r ∞), (1) E r 0 for standard locking methods, (2) E l 1/p for strict priority scheduling methods, and (3) E r ∞ for optimistic methods. Also found are bounds on E in the case where conflicts are analyzed so as to maximize E. The predictions of the random graph model are confirmed by simulations of an abstract transaction processing system. In practice, though, there is a price to pay for the increased effective level of concurrency of methods (2) and (3): using these methods there is more wasted work (i.e., more steps executed by transactions that are later aborted). In response to this problem, three new concurrency control methods suggested by the random graph model analysis are developed. Two of these, called (a) running priority and (b) older or running priority, are shown by the simulation results to perform better than the previously known methods (l)-(3) for relatively large n or large p, in terms of achieving a high effective level of concurrency at a comparatively small cost in wasted work.


In-DBMS Sampling-based Sub-trajectory Clustering.


title: Efficient and fast multi-modal foreground-background segmentation using RGBD data

abstract: Abstract This paper addresses the problem of foreground and background segmentation. Multi-modal data specifically RGBD data has gain many tasks in computer vision recently. However, techniques for background subtraction based only on single-modality still report state-of-the-art results in many benchmarks. Succeeding the fusion of depth and color data for this task requires a robust formalization allowing at the same time higher precision and faster processing. To this end, we propose to make use of kernel density estimation technique to adapt multi-modal data. To speed up kernel density estimation, we explore the fast Gauss transform which allows the summation of a mixture of M kernel at N evaluation points in O(M+N) time as opposed to O(MN) time for a direct evaluation. Extensive experiments have been carried out on four publicly available RGBD foreground/background datasets. Results demonstrate that our proposal outperforms state-of-the-art methods for almost all of the sequences acquired in challenging indoor and outdoor contexts with a fast and non-parametric operation.


title: Evolutionary algorithm in Forex trade strategy generation

abstract: This paper shows an evolutionary algorithm application to generate profitable strategies to trade futures contracts on foreign exchange market (Forex). Strategy model in approach is based on two decision trees, responsible for taking the decisions of opening long or short positions on Euro/US Dollar currency pair. Trees take into consideration only technical analysis indicators, which are connected by logic operators to identify border values of these indicators for taking profitable decision(s). We have tested the efficiency of presented approach on learning and test time-frames of various characteristics.


title: Self-other representations and relational and overt aggression in adolescent girls and boys

abstract: Aggressive behavior in girls has received far less attention than similar problems in boys. This study examined self-representation, and others' representation of self, as predictors of relational aggression, overt aggression, and assaultive behavior in 32 girls and 52 boys, 10 to 17 years of age, referred for assessment due to significant aggressive and delinquent behavior problems. As predicted, negativity of self-representation predicted relational aggression in girls but not boys. Negativity of self-representation also predicted overt aggression and assaultive behavior in both girls and boys. Parental representations of self were not predictive in this sample; however, negativity of peer representations of self, was associated with increased relational aggression in girls and decreased relational aggression in boys. Negativity of peer representations of self also predicted overt aggression and assaultive behavior in both girls and boys. Results suggest that the evaluation of self–other representations may be valuable in the assessment of risk for gender specific patterns of aggression. Copyright © 2001 John Wiley & Sons, Ltd.


title: A Taxonomy of Audiovisual Fake Multimedia Content Creation Technology

abstract: The spread of fake and misleading multimedia content on social media has become commonplace and is effecting society and its decision procedures negatively in many ways. One special case of exploiting fake content is where the deceiver uses the credibility of a trustworthy source as the means of spreading disinformation. Thanks to advancements in technology, the creation of such content is becoming possible in audiovisual form with limited technical knowledge and at low cost. The potential harm of circulation of these content in media calls for the development of automated detection methods. This paper offers a categorization of such fake content creation technology in an attempt to facilitate further study on generalized countermeasures for their detection.


Social Media as a Marketing Tool: A Literature Review


title: An Ambient Assisted Living Technology Platform for Informal Carers of the Elderly.

abstract: Family care is the most accepted and preferred care setting for both long-term care patients and their relatives. However, many of these caregivers are elderly people themselves, and often reach the point where they also need support. Care poses a substantial burden, so often it is not the health of the patient but the overload of stress for the caregiver that results in the need for much more expensive professional care and even residential care. An ambient assisted living technology platform is developed to support both older adults and their carers to overcome the challenges of the care. The platform offers informal carers support by means of monitoring Activities of Daily Care as well as their psychological state, and will provide orientation to help them improve the care given. Monitored information will be registered by means of home-installed and personal sensor technologies based on Internet of Things (IoT), which will be as unobtrusive as possible for the house inhabitants.


title: DOUBLE STANDARDS IN INTERNATIONAL ORGANIZATIONS: A COMPARATIVE STUDY OF THE UN RESPONSE TO IRAQI INVASIONS OF IRAN AND KUWAIT

abstract: Iraqi invasion of Iran in 1980 which led to eight years of full scale war, and then Iraqi invasion of Kuwait in 1990 are two examples of classic wars in 20 th century. Among other things, one of the most important issues related to theses two wars is the reaction of international organizations to them. Undoubtedly, the UN role in dealing with Iran-Iraq war, and the role played by the UN in the aftermath of Iraqi Invasion of Kuwait in 1990 up to the American occupation of Iraq in 2003 are regarded as the most important epochs in the life of the United Nations. This article is an attempt to explore and discuss the role of the United Nations in these two conflicts in a comparative perspective. It is also the aim of this paper to show the sharp differences in the ways that the UN reacted to these cases and then examine that how this double standard behavior undermined the UN credibility and damaged its image in the world.


title: Improving privacy and security in multi-authority attribute-based encryption

abstract: Attribute based encryption (ABE) [13] determines decryption ability based on a user's attributes. In a multi-authority ABE scheme, multiple attribute-authorities monitor different sets of attributes and issue corresponding decryption keys to users, and encryptors can require that a user obtain keys for appropriate attributes from each authority before decrypting a message. Chase [5] gave a multi-authority ABE scheme using the concepts of a trusted central authority (CA) and global identifiers (GID). However, the CA in that construction has the power to decrypt every ciphertext, which seems somehow contradictory to the original goal of distributing control over many potentially untrusted authorities. Moreover, in that construction, the use of a consistent GID allowed the authorities to combine their information to build a full profile with all of a user's attributes, which unnecessarily compromises the privacy of the user. In this paper, we propose a solution which removes the trusted central authority, and protects the users' privacy by preventing the authorities from pooling their information on particular users, thus making ABE more usable in practice.


title: VizWiz: nearly real-time answers to visual questions

abstract: Visual information pervades our environment. Vision is used to decide everything from what we want to eat at a restaurant and which bus route to take to whether our clothes match and how long until the milk expires. Individually, the inability to interpret such visual information is a nuisance for blind people who often have effective, if inefficient, work-arounds to overcome them. Collectively, however, they can make blind people less independent. Specialized technology addresses some problems in this space, but automatic approaches cannot yet answer the vast majority of visual questions that blind people may have. VizWiz addresses this shortcoming by using the Internet connections and cameras on existing smartphones to connect blind people and their questions to remote paid workers' answers. VizWiz is designed to have low latency and low cost, making it both competitive with expensive automatic solutions and much more versatile.


title: Advanced flip chip package on package technology for mobile applications

abstract: With the rapid technology developments in the semiconductor industry, the mobile phone has evolved from a simple communication device to a complicated and highly integrated system with multiple functions and heterogeneous devices. Based on the fast growth in emerging markets for mobile applications, many wireless devices have jumped to 4G Long Term Evolution (LTE) communication platform and may ramp to 5G generation technology in the next two to three years. Moving forward with this trend, packaging semiconductor devices for mobile electronics is more challenging than ever before. As the demand of higher performance, higher bandwidth, lower power consumption and smaller form factor are increasingly required in portable mobile devices, standard flip chip packages such as fcCSP are not able to meet these requirements. Three-dimensional (3D) package-on-package solution with flip chip interconnect (fcPoP) has been widely utilized to successfully achieve these goals. With the ability to stack a logic processor and memory device in a single package, the utilization of fcPoP is becoming a preferred solution in the mobile market segment. It is well known that fcPoP technology developments are related to the evolution of mobile low power double data rate (LPDDR) memory, requiring lower voltage, lower power consumption and higher transition rates, a significant number of 4G LTE mobile devices are adopting fcPoP with LPDDR3 and are now migrating to LPDDR4 as well as finer silicon nodes (below 20nm). Challenges in finer pitch memory devices, thinner package profiles, stringent package warpage/coplanarity requirements and Surface Mount Technology (SMT) processes are successfully met with fcPoP technology. This paper reports the 3D package developments with flip chip technology, including bare die package-on-package (BD-PoP), molded laser package-on-package (MLP-PoP) as well as interposer package-on-package (I-PoP) to support high bandwidth or wide I/O memory. Various fcPoP vehicles have been designed and fabricated to demonstrate low profile (total package height less than 1.2mm) and much finer top ball pitch solutions for mobile applications. Package warpage/coplanarity control and reliability characterization are also illustrated. These results show fcPoP architecture is an enabling technology for highly integrated, miniaturized, low profile and cost-effective 3D packaging solutions