title: Rural area deployment of internet of things connectivity: LTE and LoRaWAN case study

abstract: Actually many Internet of Things (IoT) applications and architectures are being deployed in order to support urban area applications, as for example smart cities. However, other important applications are being deployed in rural areas, as for example agribusiness, in which wireless connectivity is one of the most important issues because wireless broadband commercial technologies are not sustainable from the business model view. In order to attend the actual demand of rural areas, the present work proposes two study cases that optimize coverage and low energy consumption joining LTE and LoRaWAN wireless access technologies. The proposal architecture is based on an element called Intelligent Terminal (IT), which is a gateway that is composed by LTE and LoRaWAN interfaces. The first study case represents a terrestrial vehicular case and the second one represents an unmanned aerial vehicle (UAV) case. The terrestrial study case is used as a reference to get a quantified gain of using UAVs. Considering that every scenario has particularities in terrestrial relief characteristics, it was obtained coverage information of five scenarios with different radius. When the radius coverage is increased, usability of UAV becomes more important. In average, for radius equal to 45 km and an UAV height equal to 50 m the gain of using UAVs is equal to 2 times the coverage than using a terrestrial solution. Other important contribution are parameters of a linear equation that estimate the average coverage gain based on any arbitrary UAV altitude.


title: A Fusion Approach for Efficient Human Skin Detection

abstract: A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.


title: Shape function-based kinematics and dynamics for variable length continuum robotic arms

abstract: This paper presents a new three dimensional kinematic and dynamic model for variable length continuum arm robotic structures using a novel shape function-based approach. The model incorporates geometrically constrained structure of the arm to derive its deformation shape function. It is able to simulate spatial bending, pure elongation, and incorporates a new stiffness control feature. The model is validated through numerical simulations, based on a prototype continuum arm, that yields physically accurate results.


title: Clock network design for ultra-low power applications

abstract: Robust design is a critical concern in ultra-low voltage operation due to large sensitivity to process and environmental variations. In particular, clock networks need careful attention to ensure robust distribution of well-defined clock signals to avoid setup and hold time violations. In this paper, we investigate the design methodology of robust clock networks for ultra-low voltage applications. A case study shows that an optimally-chosen clock network improves skew variation by 36× and energy consumption by 49%, compared to a typical clock network. Additionally, the impact of supply voltage and technology scaling on the optimal clock network construction is investigated.


title: Linear Algebraic Structure of Word Senses, with Applications to Polysemy

abstract: Word embeddings are ubiquitous in NLP and information retrieval, but it's unclear what they represent when the word is polysemous, i.e., has multiple senses. Here it is shown that multiple word senses reside in linear superposition within the word embedding and can be recovered by simple sparse coding. ::: The success of the method ---which applies to several embedding methods including word2vec--- is mathematically explained using the random walk on discourses model (Arora et al., 2015). A novel aspect of our technique is that each word sense is also accompanied by one of about 2000 "discourse atoms" that give a succinct description of which other words co-occur with that word sense. Discourse atoms seem of independent interest, and make the method potentially more useful than the traditional clustering-based approaches to polysemy.


title: Frequency-division multiplexing in the terahertz range using a leaky-wave antenna

abstract: Using a leaky-wave antenna, free-space-to-waveguide frequency-division multiplexing and demultiplexing are demonstrated in the terahertz range. Both the frequency and the spectral bandwidth of multiplexed channels can be independently controlled.


title: A practical and systematic review of Weibull statistics for reporting strengths of dental materials

abstract: Objectives. To review the history, theory and current applications of Weibull analyses sufficient to make informed decisions regarding practical use of the analysis in dental material strength testing.


title: Conditional Generative Moment-Matching Networks

abstract: Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this paper, we present conditional generative moment- matching networks (CGMMN), which learn a conditional distribution given some input variables based on a conditional maximum mean discrepancy (CMMD) criterion. The learning is performed by stochastic gradient descent with the gradient calculated by back-propagation. We evaluate CGMMN on a wide range of tasks, including predictive modeling, contextual generation, and Bayesian dark knowledge, which distills knowledge from a Bayesian model by learning a relatively small CGMMN student network. Our results demonstrate competitive performance in all the tasks.


title: Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users

abstract: Emojis have been widely used to simplify emotional expression and enrich user experience. As an interesting practice of ubiquitous computing, emojis are adopted by Internet users from many different countries, on many devices (particularly popular on smartphones), and in many applications. The "ubiquitous" usage of emojis enables us to study and compare user behaviors and preferences across countries and cultures. We present an analysis on how smartphone users use emojis based on a very large data set collected from a popular emoji keyboard. The data set contains a complete month of emoji usage of 3.88 million active users from 212 countries and regions. We demonstrate that the categories and frequencies of emojis used by these users provide rich signals for the identification and the understanding of cultural differences of smartphone users. Users from different countries present significantly different preferences on emojis, which complies with the well-known Hofstede's cultural dimensions model.


title: Direct Active and Reactive Power Regulation of DFIG Using Sliding-Mode Control Approach

abstract: This paper presents a new direct active and reactive power control (DPC) of grid-connected doubly fed induction generator (DFIG)-based wind turbine systems. The proposed DPC strategy employs a nonlinear sliding-mode control scheme to directly calculate the required rotor control voltage so as to eliminate the instantaneous errors of active and reactive powers without involving any synchronous coordinate transformations. Thus, no extra current control loops are required, thereby simplifying the system design and enhancing the transient performance. Constant converter switching frequency is achieved by using space vector modulation, which eases the designs of the power converter and the ac harmonic filter. Simulation results on a 2-MW grid-connected DFIG system are provided and compared with those of classic voltage-oriented vector control (VC) and conventional lookup table (LUT) DPC. The proposed DPC provides enhanced transient performance similar to the LUT DPC and keeps the steady-state harmonic spectra at the same level as the VC strategy.


title: Extended Gardner Detector for Improved Symbol-Timing Recovery of $M$-PSK Signals

abstract: In a recent paper, the modified Gardner detector has been proposed for advanced symbol-timing recovery of M-ary phase-shift keying signals. Compared with the original algorithm, the self-noise jitter can be considerably reduced for highly bandlimited systems and smaller values of M. In this letter, a completely different approach is investigated, which improves the self-noise performance for larger values of M, as well


title: A 0.19-V minimum input low energy level shifter for extremely low-voltage VLSIs

abstract: In this paper, we propose a low-power level shifter (LS) capable of converting extremely low-input voltage into highoutput voltage. The proposed LS consists of a pre-amplifier with a logic error correction circuit and an output latch stage. The pre-amplifier generates complementary amplified signals, and the latch stage converts them into full-swing output signals. Simulated results demonstrated that the proposed LS in a 0.18-μm CMOS process can convert a 0.19-V input into 1.8-V output correctly. The energy and the delay time of the proposed LS were 0.24 pJ and 21.4 ns when the low supply voltage, high supply voltage, and the input pulse frequency, were 0.4, 1.8 V, and 100 kHz, respectively.


title: Automatic estimation of parkinson's disease severity from diverse speech tasks.

abstract: The need for reliable, scalable and efficient diagnosis of Parkinson’s Disease (PD) is a major clinical need. Automating the diagnosis can lead to more accurate and objective predictions as well as provide insights regarding the nature of Parkinson’s condition. This paper proposes a fully automated system to rate the severity (UPDRS-III scale) of PD from patients’ speech. Specifically, the system captures atypicalities in an individual’s voice when performing multiple diverse speaking tasks and makes a unified prediction of the PD severity. The performance is tested in a cross-data setting, with different subjects and dissimilar recording conditions. Results indicate that (i) effective features vary depending on the nature of the specific speech task, (ii) additional novel feature sets to detect distortions in Parkinson’s speech significantly improve the prediction accuracy from the Interspeech15 Challenge baseline system and (iii) our fusion system based on an unsupervised clustering technique also improves the accuracy. Our system incorporates ivector and functionals for segmental features, non-linear time series features, speech rhythm and automatic speech recognition decoding based features. By its application on the Interspeech15 eating condition challenge, the system also shows its potential for detecting other sources of speech variability.


title: Abdominal Tumor in a 14-Year-Old Adolescent: Imperforate Hymen, Resulting in Hematocolpos—A Case Report and Review of the Literature

abstract: Background. Abdominal masses in female adolescents are uncommon. A rare cause of this condition is hematocolpos due to imperforate hymen. Case. We present a case of an unusually massive asymptomatic abdominal bulk in a 14-year-old female patient, who sought for medical advice after unusual abdominal pain lasting for few weeks. The patient was otherwise asymptomatic, apart from an unusual dramatic expansion of her abdominal wall during the last month. We describe the surgical management and the follow-up of the patient. Summary and Conclusion. Clinicians should keep in mind that an imperforate hymen can cause abdominal growth due to hematocolpos and include it in the differential diagnosis of such a clinical entity in female adolescents. 2D ultrasound is usually efficient for the confirmation of the diagnosis of hematocolpos, but 3D ultrasound is more accurate. Wide excision should be undertaken, as an initial approach, to avoid recurrence.


title: Soft DBSCAN: Improving DBSCAN clustering method using fuzzy set theory

abstract: Clustering is one of the most valuable methods of computational intelligence field, in which sets of related objects are cataloged into clusters. Almost all of the well-known clustering algorithms require input number of clusters which is hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough towards noisy data. In contrast, density based methods, such as DBSCAN, have obvious advantages over explicit samples. They discover the number of clusters, as well as, they detect noises. Additionally, the shape of such clusters can also be irregular. However, they have difficulties in handling the challenges posed by the collection of natural data which is often vague. This paper presents an efficient clustering technique, named “Soft DBSCAN” that combines DBSCAN and fuzzy set theory. Our new method is galvanized by Fuzzy C Means in the way of using the fuzzy membership functions. The results of our method show that it is efficient not only in handling noises, contrary to Fuzzy C Means, but also, able to assign one data point into more than one cluster. Simulative experiments are carried out on a variety of datasets, throughout different evaluation's criteria, which highlight the soft DBSCAN's effectiveness and cluster validity to check the good quality of clustering results.


title: A scaled version of the General Health Questionnaire

abstract: This study reports the factor structure of the symptoms comprising the General Health Questionnaire when it is completed in a primary care setting. A shorter, 28-item GHQ is proposed consisting of 4 subscales: somatic symptoms, anxiety and insomnia, social dysfunction and severe depression. Preliminary data concerning the validity of these scales are presented, and the performance of the whole 28-item questionnaire as a screening test is evaluated. The factor structure of the symptomatology is found to be very similar for 3 independent sets of data.


title: Multimodal Trajectory Playback for Teaching Shape Information and Trajectories to Visually Impaired Computer Users

abstract: There are difficulties in presenting nontextual or dynamic information to blind or visually impaired users through computers. This article examines the potential of haptic and auditory trajectory playback as a method of teaching shapes and gestures to visually impaired people. Two studies are described which test the success of teaching simple shapes. The first study examines haptic trajectory playback alone, played through a force-feedback device, and compares performance of visually impaired users with sighted users. It demonstrates that the task is significantly harder for visually impaired users. The second study builds on these results, combining force-feedback with audio to teach visually impaired users to recreate shapes. The results suggest that users performed significantly better when presented with multimodal haptic and audio playback of the shape, rather than haptic only. Finally, an initial test of these ideas in an application context is described, with sighted participants describing drawings to visually impaired participants through touch and sound. This study demonstrates in what situations trajectory playback can prove a useful role in a collaborative setting.


title: The SP theory of intelligence: benefits and applications

abstract: This article describes existing and expected benefits of the SP theory ofintelligence, and some potential applications. The theory aims to simplify and integrate ideasacross artificial intelligence, mainstream computing, and human perception and cognition,with information compression as a unifying theme. It combines conceptual simplicitywith descriptive and explanatory power across several areas of computing and cognition.In the SP machine—an expression of the SP theory which is currently realized in theform of a computer model—there is potential for an overall simplification of computingsystems, including software. The SP theory promises deeper insights and better solutions inseveral areas of application including, most notably, unsupervised learning, natural languageprocessing, autonomous robots, computer vision, intelligent databases, software engineering,information compression, medical diagnosis and big data. There is also potential inareas such as the semantic web, bioinformatics, structuring of documents, the detection ofcomputer viruses, data fusion, new kinds of computer, and the development of scientifictheories. The theory promises seamless integration of structures and functions within andbetween different areas of application. The potential value, worldwide, of these benefits andapplications is at least $190 billion each year. Further development would be facilitatedby the creation of a high-parallel, open-source version of the SP machine, available toresearchers everywhere.


Neural Sequence-Labelling Models for Grammatical Error Correction


title: A database for emotional interactions of the elderly

abstract: Emotional interaction plays an important role in human-computer interaction domains. One of the major limitations in the study of emotion interaction is the lack of databases. This paper describes a database for emotion interactions of the elderly. The database was collected with audio and video from sixteen actors (8 female and 8 male) in daily conversations of TV series, which covers seven type emotions, namely, anger, boredom, happiness, sadness, surprise, neutrality and disgust. The database consists of 810 speech-video excerpts from 118 conversations. Emotion annotations were evaluated by 18 participants (10 men and 8 women). In addition, this databases record the transitions of actors' emotion in daily conversations.


title: Large scale evolution of convolutional neural networks using volunteer computing

abstract: This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been implemented as part of a BOINC volunteer computing project, allowing large scale evolution. During a period of two months, over 4,500 volunteered computers on the Citizen Science Grid trained over 120,000 CNNs and evolved networks reaching 98.32% test data accuracy on the MNIST handwritten digits dataset. These results are even stronger as the backpropagation strategy used to train the CNNs was fairly rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these were initial test runs done without refinement of the backpropagation hyperparameters. Further, the EXACT evolutionary strategy is independent of the method used to train the CNNs, so they could be further improved by advanced techniques like elastic distortions, pretraining and dropout. The evolved networks are also quite interesting, showing "organic" structures and significant differences from standard human designed architectures.


title: Dynamic resource allocations based on Q-learning for D2D communication in cellular networks

abstract: In order to solve the problem of spectrum and power allocation for D2D communication in cellular networks when the prior knowledge is not available, a method based on machine learning is proposed in this paper. Q-learning, which is one of the most important algorithms in machine learning, is proposed to solve the radio resource management in underlay mode to find the optimal strategy in time series. In this mode, Q-learning is used to finish the channel assignment and the power allocation at the same time. And the simulation results show that greater system capacity can be achieved through the method proposed in this paper.


title: Ensemble Learning for Effective Run-Time Hardware-Based Malware Detection: A Comprehensive Analysis and Classification

abstract: Malware detection at the hardware level has emerged recently as a promising solution to improve the security of computing systems. Hardware-based malware detectors take advantage of Machine Learning (ML) classifiers to detect pattern of malicious applications at run-time. These ML classifiers are trained using low-level features such as processor Hardware Performance Counters (HPCs) data which are captured at run-time to appropriately represent the application behaviour. Recent studies show the potential of standard ML-based classifiers for detecting malware using analysis of large number of microarchitectural events, more than the very limited number of HPC registers available in today’s microprocessors which varies from 2 to 8. This results in executing the application more than once to collect the required data, which in turn makes the solution less practical for effective run-time malware detection. Our results show a clear trade-off between the performance of standard ML classifiers and the number and diversity of HPCs available in modern microprocessors. This paper proposes a machine learning-based solution to break this trade-off to realize effective run-time detection of malware. We propose ensemble learning techniques to improve the performance of the hardware-based malware detectors despite using a very small number of microarchitectural events that are captured at run-time by existing HPCs, eliminating the need to run an application several times. For this purpose, eight robust machine learning models and two well-known ensemble learning classifiers applied on all studied ML models (sixteen in total) are implemented for malware detection and precisely compared and characterized in terms of detection accuracy, robustness, performance (accuracy × robustness), and hardware overheads. The experimental results show that the proposed ensemble learning-based malware detection with just 2 HPCs using ensemble technique outperforms standard classifiers with 8 HPCs by up to 17%. In addition, it can match the robustness and performance of standard ML-based detectors with 16 HPCs while using only 4 HPCs allowing effective run-time detection of malware.


title: A practice-oriented framework for measuring privacy and utility in data sanitization systems

abstract: Published data is prone to privacy attacks. Sanitization methods aim to prevent these attacks while maintaining usefulness of the data for legitimate users. Quantifying the trade-off between usefulness and privacy of published data has been the subject of much research in recent years. We propose a pragmatic framework for evaluating sanitization systems in real-life and use data mining utility as a universal measure of usefulness and privacy. We propose a definition for data mining utility that can be tuned to capture the needs of data users and the adversaries' intentions in a setting that is specified by a database, a candidate sanitization method, and privacy and utility concerns of data owner. We use this framework to evaluate and compare privacy and utility offered by two well-known sanitization methods, namely k-anonymity and e-differential privacy, when UCI's "Adult" dataset and the Weka data mining package is used, and utility and privacy measures are defined for users and adversaries. In the case of k-anonymity, we compare our results with the recent work of Brickell and Shmatikov (KDD 2008), and show that using data mining algorithms increases their proposed adversarial gains.


title: Vision-Based Autonomous Robot Self-Docking and Recharging

abstract: This paper presents a method for autonomous recharging of a mobile robot, a necessity for achieving long-term robotic activity without human intervention. A recharging station is designed consisting of a stationary docking station and a docking mechanism mounted to an ER-1 Evolution Robotics robot. The docking station and docking mechanism serve as a dual-power source, providing a mechanical and electrical connection between the recharging system of the robot and a laptop placed on it. Docking strategy algorithms use vision based navigation. The result is a significantly low-cost, high-entrance angle tolerant system. Iterative improvements to the system, to resist environmental perturbations and implement obstacle avoidance, ultimately resulted in a docking success rate of 100 percent over 50 trials.


title: On Human Motion Prediction Using Recurrent Neural Networks

abstract: Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state-of-the-art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which result in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.


title: Long-Term Exposure to Air Pollution and Incidence of Cardiovascular Events in Women

abstract: BACKGROUND ::: Fine particulate air pollution has been linked to cardiovascular disease, but previous studies have assessed only mortality and differences in exposure between cities. We examined the association of long-term exposure to particulate matter of less than 2.5 microm in aerodynamic diameter (PM2.5) with cardiovascular events. ::: ::: ::: METHODS ::: We studied 65,893 postmenopausal women without previous cardiovascular disease in 36 U.S. metropolitan areas from 1994 to 1998, with a median follow-up of 6 years. We assessed the women's exposure to air pollutants using the monitor located nearest to each woman's residence. Hazard ratios were estimated for the first cardiovascular event, adjusting for age, race or ethnic group, smoking status, educational level, household income, body-mass index, and presence or absence of diabetes, hypertension, or hypercholesterolemia. ::: ::: ::: RESULTS ::: A total of 1816 women had one or more fatal or nonfatal cardiovascular events, as confirmed by a review of medical records, including death from coronary heart disease or cerebrovascular disease, coronary revascularization, myocardial infarction, and stroke. In 2000, levels of PM2.5 exposure varied from 3.4 to 28.3 microg per cubic meter (mean, 13.5). Each increase of 10 microg per cubic meter was associated with a 24% increase in the risk of a cardiovascular event (hazard ratio, 1.24; 95% confidence interval [CI], 1.09 to 1.41) and a 76% increase in the risk of death from cardiovascular disease (hazard ratio, 1.76; 95% CI, 1.25 to 2.47). For cardiovascular events, the between-city effect appeared to be smaller than the within-city effect. The risk of cerebrovascular events was also associated with increased levels of PM2.5 (hazard ratio, 1.35; 95% CI, 1.08 to 1.68). ::: ::: ::: CONCLUSIONS ::: Long-term exposure to fine particulate air pollution is associated with the incidence of cardiovascular disease and death among postmenopausal women. Exposure differences within cities are associated with the risk of cardiovascular disease.


title: Detecting Communities in Large Networks by Iterative Local Expansion

abstract: Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected groups of vertices, with only sparser connections between groups. Identifying community structure in networks has attracted much research attention. However, most existing approaches require structure information of the graph in question to be completely accessible, which is impractical for some large networks, e.g., the World Wide Web (WWW). In this paper, we propose a community discovery algorithm for large networks that iteratively finds communities based on local information only. We compare our algorithm with previous global approaches to show its scalability. Experimental results on real world networks, such as the co-purchase network from Amazon, verify the feasibility and effectiveness of our approach.


title: Longest Common Subsequences in Permutations and Maximum Cliques in Circle Graphs

abstract: For two strings a, b, the longest common subsequence (LCS) problem consists in comparing a and b by computing the length of their LCS . In a previous paper, we defined a generalisation, called “the all semi-local LCS problem”, for which we proposed an efficient output representation and an efficient algorithm. In this paper, we consider a restriction of this problem to strings that are permutations of a given set. The resulting problem is equivalent to the all local longest increasing subsequences (LIS) problem. We propose an algorithm for this problem, running in time O(n1.5) on an input of size n. As an interesting application of our method, we propose a new algorithm for finding a maximum clique in a circle graph on n nodes, running in the same asymptotic time O(n1.5). Compared to a number of previous algorithms for this problem, our approach presents a substantial improvement in worst-case running time.


title: Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex

abstract: Object recognition is challenging because each object produces myriad retinal images. Responses of neurons from the inferior temporal cortex (IT) are selective to different objects, yet tolerant (“invariant”) to changes in object position, scale, and pose. How does the brain construct this neuronal tolerance? We report a form of neuronal learning that suggests the underlying solution. Targeted alteration of the natural temporal contiguity of visual experience caused specific changes in IT position tolerance. This unsupervised temporal slowness learning (UTL) was substantial, increased with experience, and was significant in single IT neurons after just 1 hour. Together with previous theoretical work and human object perception experiments, we speculate that UTL may reflect the mechanism by which the visual stream builds and maintains tolerant object representations.


Chapter 3 Eye Tracking and Eye-Based Human – Computer Interaction


title: Gangliosides are neuronal ligands for myelin-associated glycoprotein.

abstract: Nerve cells depend on specific interactions with glial cells for proper function. Myelinating glial cells are thought to associate with neuronal axons, in part, via the cell-surface adhesion protein, myelin-associated glycoprotein (MAG). MAG is also thought to be a major inhibitor of neurite outgrowth (axon regeneration) in the adult central nervous system. Primary structure and in vitro function place MAG in an immunoglobulin-related family of sialic acid-binding lactins. We report that a limited set of structurally related gangliosides, known to be expressed on myelinated neurons in vivo, are ligands for MAG. When major brain gangliosides were adsorbed as artificial membranes on plastic microwells, only GT1b and GD1a supported cell adhesion of MAG-transfected COS-1 cells. Furthermore, a quantitatively minor ganglioside expressed on cholinergic neurons, GQ1b alpha (also known as Chol-1 alpha-b), was much more potent than GT1b or GD1a in supporting MAG-mediated cell adhesion. Adhesion to either GT1b or GQ1b alpha was abolished by pretreatment of the adsorbed gangliosides with neuraminidase. On the basis of structure-function studies of 19 test glycosphingolipids, an alpha 2,3-N-acetylneuraminic acid residue on the terminal galactose of a gangliotetraose core is necessary for MAG binding, and additional sialic acid residues linked to the other neutral core saccharides [Gal(II) and GalNAc(III)] contribute significantly to binding affinity. MAG-mediated adhesion to gangliosides was blocked by pretreatment of the MAG-transfected COS-1 cells with anti-MAG monoclonal antibody 513, which is known to inhibit oligodendrocyte-neuron binding. These data are consistent with the conclusion that MAG-mediated cell-cell interactions involve MAG-ganglioside recognition and binding.


Web-based recruiting Framework for CV structuring


title: A Scandinavian challenge, a US response: methodological assumptions in Scandinavian and US prototyping approaches

abstract: In the early 1980s, Scandinavian software designers who sought to make systems design more participatory and democratic turned to prototyping. The "Scandinavian challenge" of making computers more democratic inspired others who became interested in user-centered design; information designers on both sides of the Atlantic began to employ prototyping as a way to encourage user participation and feedback in various design approaches. But, as European and North American researchers have pointed out, prototyping is seen as meeting very different needs in Scandinavia and in the US. Thus design approaches that originate on either side of the Atlantic have implemented prototyping quite differently, have deployed it to meet quite different goals, and have tended to understand prototyping results in different ways.These differences are typically glossed over in technical communication research. Technical communicators have lately become quite excited about prototyping's potential to help design documentation, but the technical communication literature shows little critical awareness of the methodological differences between Scandinavian and US prototyping. In this presentation, I map out some of these differences by comparing prototyping in a variety of design approaches originating in Scandinavia and the US, such as mock-ups, cooperative prototyping, CARD, PICTIVE, and contextual design. Finally, I discuss implications for future technical communication research involving prototyping.


title: Computational Thinking in Elementary and Secondary Teacher Education

abstract: Computational thinking (CT) is broadly defined as the mental activity for abstracting problems and formulating solutions that can be automated. In an increasingly information-based society, CT is becoming an essential skill for everyone. To ensure that students develop this ability at the K-12 level, it is important to provide teachers with an adequate knowledge about CT and how to incorporate it into their teaching. This article describes a study on designing and introducing computational thinking modules and assessing their impact on preservice teachers’ understanding of CT concepts, as well as their attitude towards computing. Results demonstrate that introducing computational thinking into education courses can effectively influence preservice teachers’ understanding of CT concepts.


title: Investigating the Potential of the Flipped Classroom Model in K-12 ICT Teaching and Learning: An Action Research Study

abstract: Introduction Information and Communication Technologies (ICT) curricula are a core priority of educational policies worldwide, for cultivating students' diverse digital competences (Commonwealth of Australia, 2010; US Department of Education, 2010; Eurydice, 2012). These digital competences are considered a core strand of the 21st century skillset and industry work-force requirements are shifted towards attaining and exploiting such competences (OECD, 2014). Thus, ICT educational curricula have been increasingly required to effectively cultivate student digital competences by promoting more student-centred teaching approaches (e.g., Balanskat & Engelhardt, 2015). For example, the project-based approach has been positively evaluated in the context of K-12 education as a promising method to effectively engage students in the learning process and to develop their digital competences (e.g., Wang et al., 2016; Tsai et al., 2015). Furthermore, on top of exploiting such student-centered teaching approaches, educational innovations such as the Flipped Classroom Model (FCM) have also been studied for improving learning experiences and competences. The FCM has been proposed as a method to maximize the use of teacher-supported face-to-face classroom-based sessions towards delivering hands-on activities and individual scaffolding. To achieve this, it opts for substituting the traditional teachers' lecture with appropriately designed educational resources which can be engaged by the student in an autonomous manner (Bergmann & Sams, 2012). The FCM has received a growing level of research attention given the promising results that showcase its capacity to enhance teaching practice and deliver (among others) better students' cognitive learning outcomes and motivation in a wide range of subjects and educational levels, including ICT and STEM (Giannakos et al., 2014). However, regarding ICT teaching, existing FCM research has been solely addressed in Higher Education, where a large body of research exists that provides positive evidence on the potential of FCM to provide enhanced student experiences. Despite this evidence, however, no explicit focus has yet been placed on evaluating the FCM in the context of K-12 ICT teaching. This presents an important challenge, especially considering the range of successful implementations of the FCM in other compulsory school education subjects. Thus, the contribution of this paper is to address the aforementioned issue and evaluate the FCM within the context of K-12 ICT curriculum. More specifically, the work presents the design and implementation of an action research for evaluating the capacity of the FCM to enhance students' learning experiences from a range of perspectives, namely (a) cognitive learning outcomes, (b) distribution of different types of learning activities during the face-to-face sessions, (c) levels of motivation during the learning activities, and (d) level of students' engagement during the learning activities. The remainder of the paper is structured as follows. The Background section defines the FCM and describes existing research work on its implementation in Higher Education to enhance students' learning experiences. The Research Methodology section presents the design and methodology of the action research. The Results section presents the findings of the action research in terms of the four research questions defined. Finally, the Discussion and Future Work section presents the conclusions drawn and outlines potential strands of further research. Background The Flipped Classroom Model (FCM) is an emerging blended learning model, which argues for improving the student-centered exploitation of face-to-face sessions, by minimizing teacher lecture and increasing students' active learning and collaboration (Bergmann & Sams, 2012). Teacher-facilitated face-to-face sessions can provide students with unique learning experiences through direct access to both their classmates (for collaborative activities) as well as to feedback and scaffolding by their teacher (DeLozier & Rhodes, 2016). …


title: Organizational ambidexterity: exploring the knowledge base

abstract: The present research work shows the results of an analysis about the existing literature on one of the `topics' which is currently raising greater interest among scholars and researchers in the fields of strategic management and organization science, namely: organizational ambidexterity. More precisely, and seeking to identify and visualize the intellectual structure or knowledge base of the research developed in relation to this construct, a decision was made to analyze a total of 283 research papers which appeared after the publication in the journal California Management Review in the summer of 1996 of the seminal work by Tushman and O'Reilly III entitled `Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change,' where these authors suggested that organizations need to explore and exploit simultaneously if they want to be ambidextrous. As for the methodology applied, it was based on the utilization of bibliometric techniques--particularly citation analyses and author co-citation analyses and social networks analysis.


Convolutional Color Constancy Supplemental Material


title: A new concept of skeletal dysplasias

abstract: The skeletal dysplasias form a large group of hereditary disorders characterized by abnormal growth and malformations of bone and cartilage. The clinical severity ranges from mildly affected short stature to lethal forms. Spranger classified approximately 200 different skeletal dysplasias in 1992, based on the clinical and radiographic features and the mode of genetic transmission. Etiopathogenesis of skeletal dysplasias is better documented now, with rapid accumulation of knowledge concerning defective genes and proteins causing this group of disorders. Mutations responsible for skeletal dysplasias may cause defects in the synthesis of structural proteins and in metabolic pathways, degradation of macromolecules, growth factors and receptors and transcription factors. Classifications by the International Working Group on Constitutional Diseases of Bone were based on the mutations in the same group gene taking into consideration the clinical and radiological findings (achondroplasia group, dysplasia with decreased bone density group and type II collagenopathies...). Clinical manifestations and radiological investigations are crucial for the differential diagnosis in skeletal dysplasias. However, prenatal diagnosis and postnatal definitive diagnosis are most often achieved by molecular analysis of the patient. Therefore, these groups of disorders require management by a multidisciplinary team of specialists, including pediatricians, genetic specialists, orthopedists and psychiatrists.


title: A Computer Vision System to Localize and Classify Wastes on the Streets

abstract: Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds.


title: A low power Voice Activity Detector for portable applications

abstract: Voice Activity Detectors (VADs) are used to enhance performances and to reduce the activation rate of speech recognition and key-word spotting applications. The last aspect is crucial for portable applications because it allows to save energy, increasing battery life. During last decades, VADs have been realized through hardware solutions to increase their speed in processing and to reduce their power consumption. However, the hardware implementation often represents a limit on the choice of the features to use, limiting the performances on recognition. This paper shows a low-power and low-area serial logistic regression classffier which uses the frame-energy, the maximum absolute signal finite difference and the maximum absolute squared signal finite difference over a frame as features. The system has been implemented on IGLOO nano Field Programmable Gate Array (FPGA), leading to power consumption of 0.559 mW and offering acceptable performances for its use as a preprocessor for speech recognition systems or a more sophisticated software VAD.


title: Understanding student perceptions of game elements to develop gamified systems for learning

abstract: As a precursor to the development of a gamified system to improve learning outcomes, we investigate the perception of important stakeholders, namely learners, on gamification in learning. However, as gamification is a relatively new approach/term, we neither assume that learners are knowledgeable about it nor do we attempt to explain the term in detail to them. Instead, we focus on common elements between gamification and learners' gaming experience, to determine how learners perceive these game elements. This understanding of learners' perception on the matter will aid in the later development of a gamified system for learning. In this work, we survey 51 undergraduate IT students to obtain their perceptions on game elements such as points, leader boards, player profiles, progress bars, and achievement badges. We then analyse the responses and suggest how these game elements can be used in a gamified system for education. It was found that undergraduate students have a positive perception of gamification and are interested in its use for learning. They expect that gamification will make classes more interesting and will improve the learning environment. Students also believed gamification would be beneficial by assisting them to improve their understanding of course material. Our results also indicate that points and leader boards promote competition, and that points and player profiles (which includes the tracking of performance statistics) can be used to provide users with performance feedback. Students perceive progress bars as motivators to complete tasks while achievement badges are perceived to motivate task mastery. Given these results, we recommend that gamified learning systems should have a strong focus providing different types of feedback to learners, especially feedback related to progression.


title: Product lifecycle management in aviation maintenance, repair and overhaul

abstract: This publication discusses the evolution of CAD, CAM, and CAE tools through product data management systems into today's product lifecycle management (PLM), followed by a review of the characteristics and benefits of PLM. Current practices and potential applications of PLM in aviation maintenance, repair and overhaul (MRO) are discussed through case studies, two of which were from the authors' experience.


title: PTSD Elderly War Veterans: A Clinical Controlled Pilot Study

abstract: Abstract Around 25,000 war veterans in Portugal suffer posttraumatic stress disorder (PTSD). This clinically controlled study evaluates virtual reality exposure therapy (VRET) as an alternative procedure to reduce PTSD symptoms. Ten patients were assigned to three groups: VRET, exposure in imagination (EI), and waiting list (WL). The patients were Portuguese veterans from a series of wars fought in former African colonies more than 30 years ago. While the EI group participated in traditional imagination therapy, the VRET group was exposed to a virtual reality (VR) war scenario. Cues such as ambush, mortar blasting, and waiting for rescue were used in the VR. Patients enrolled in the VRET group showed statistical reduction of PTSD-associated disorders like depression and anxiety. Far from being conclusive, this pilot study nonetheless presents some promising data on the use of VRET on old war veteran populations.


title: Relationship Queries on Extended Knowledge Graphs

abstract: Entity search over text corpora is not geared for relationship queries where answers are tuples of related entities and where a query often requires joining cues from multiple documents. With large knowledge graphs, structured querying on their relational facts is an alternative, but often suffers from poor recall because of mismatches between user queries and the knowledge graph or because of weakly populated relations. This paper presents the TriniT search engine for querying and ranking on extended knowledge graphs that combine relational facts with textual web contents. Our query language is designed on the paradigm of SPO triple patterns, but is more expressive, supporting textual phrases for each of the SPO arguments. We present a model for automatic query relaxation to compensate for mismatches between the data and a user's query. Query answers -- tuples of entities -- are ranked by a statistical language model. We present experiments with different benchmarks, including complex relationship queries, over a combination of the Yago knowledge graph and the entity-annotated ClueWeb'09 corpus.


title: Cellular traffic offloading through opportunistic communications: a case study

abstract: Due to the increasing popularity of various applications for smartphones, 3G networks are currently overloaded by mobile data traffic. Offloading cellular traffic through opportunistic communications is a promising solution to partially solve this problem, because there is no monetary cost for it. As a case study, we investigate the target-set selection problem for information delivery in the emerging Mobile Social Networks (MoSoNets). We propose to exploit opportunistic communications to facilitate the information dissemination and thus reduce the amount of cellular traffic. In particular, we study how to select the target set with only k users, such that we can minimize the cellular data traffic. In this scenario, initially the content service providers deliver information over cellular networks to only users in the target set. Then through opportunistic communications, target-users will further propagate the information among all the subscribed users. Finally, service providers will send the information to users who fail to receive it before the delivery deadline (i.e., delay-tolerance threshold). We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. The simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload cellular traffic by up to 73.66% for a real-world mobility trace.


title: ESPnet: End-to-End Speech Processing Toolkit

abstract: This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.


title: Modeling Individual Differences in Game Behavior using HMM

abstract: Player modeling is an important concept that has gained much attention in game research due to its utility in developing adaptive techniques to target better designs for engagement and retention. Previous work has explored modeling individual differences using machine learning algorithms per- formed on aggregated game actions. However, players' individual differences may be better manifested through sequential patterns of the in-game player's actions. While few works have explored sequential analysis of player data, none have explored the use of Hidden Markov Models (HMM) to model individual differences, which is the topic of this paper. In par- ticular, we developed a modeling approach using data col- lected from players playing a Role-Playing Game (RPG). Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of player in-game behaviors to model individual differences, and 2. using the output of the HMM, we generate behavioral features used to classify real world players' characteristics, including game expertise and the big five personality traits. Our results show predictive power for some of personality traits, such as game expertise and conscientiousness, but the most influential factor was game expertise.


title: Multi-agent architecture for reliable Cyber-Physical Systems (CPS)

abstract: High reliability is an important characteristic of Cyber-Physical Systems (CPS). New CPS components/subsystems have to be developed for the detection, isolation, recovery and prevention of failures and external attacks. To address this challenge, we propose a multi-agent-based subsystem architecture that can detect and prevent CPS hardware components' failures. The architecture comprises of several types of agents with well-defined behaviors which cooperate to fulfil the goal of the CPS subsystem. We evaluate the performance of the proposed approach using reliability metrics for a CPS aimed at monitoring ambient and environmental parameters.


title: E-WOM and Accommodation An Analysis of the Factors That Influence Travelers’ Adoption of Information from Online Reviews

abstract: Online reviews (ORs) are continuing to foster a renewed spread of word-of-mouth in the travel industry. Travelers are increasingly using ORs to inform them about accommodations and other tourism-related products. As such, it is important to improve our understanding of the behavioral consequences of e-word-of-mouth. In this article, we adopt the elaboration likelihood model to identify what influences travelers to adopt information from ORs in their decision making. We measure the influence of six dimensions of information quality that are part of the central route and two dimensions that are associated with the peripheral route of persuasion. The results of this study reveal that product ranking, information accuracy, information value-added, information relevance, and information timeliness are strong predictors of travelers’ adoption of information from ORs on accommodations. These results imply that high-involvement travelers adopt both central (information quality) and peripheral (product ranking) ro...


title: State of the art of nanocrystals--special features, production, nanotoxicology aspects and intracellular delivery.

abstract: Drug nanocrystals are the latest, broadly introduced nanoparticulate carrier to the pharmaceutical market from the year 2000 onwards. The special features of nanocrystals for the delivery of poorly soluble drugs are briefly reviewed (saturation solubility, dissolution velocity, adhesiveness). The industrially relevant bottom up (precipitation) and top down production technologies (pearl milling, high pressure homogenization, combination technologies) are presented. As nanotoxicological aspects, the effect of size, degradability versus biopersistency and intracellular uptake are discussed, classifying the nanocrystals in the low/non-risk group. Intracellular uptake plays a minor or no role for dermal and oral nanocrystals, but it plays a key role for intravenously injected nanocrystals (e.g. nevirapine, paclitaxel, itraconazole). Uptake by the macrophages of the mononuclear phagocytic system (MPS, liver spleen) can modify/optimize blood profiles via prolonged release from the MPS (itraconazole), but also target toxicity by too high organ concentrations and thus cause nanotoxicity. The balance in the competitive intracellular uptake by MPS and the target cells (e.g. blood–brain barrier) decides about therapeutic efficiency. The concept of “differential protein adsorption” to modulate this balance is shown for its applicability to nanocrystals for intracellular delivery to the cells of the blood–brain barrier (atovaquone).


title: A Compact RDF Store Using Suffix Arrays

abstract: RDF has become a standard format to describe resources in the Semantic Web and other scenarios. RDF data is composed of triples subject,i¾źpredicate,i¾źobject, referring respectively to a resource, a property of that resource, and the value of such property. Compact storage schemes allow fitting larger datasets in main memory for faster processing. On the other hand, supporting efficient SPARQL queries on RDF datasets requires index data structures to accompany the data, which hampers compactness. As done for text collections, we introduce a self-index for RDF data, which combines the data and its index in a single representation that takes less space than the raw triples and efficiently supports basic SPARQL queries. Our storage format, RDFCSA, builds on compressed suffix arrays. Although there exist more compact representations of RDF data, RDFCSA uses about half of the space of the raw data and replaces it and displays much more robust and predictable query times around 1---2 microseconds per retrieved triple. RDFCSA is 3 orders of magnitude faster than representations like MonetDB or RDF-3X, while using the same space as the former and 6 times less space than the latter. It is also faster than the more compact representations on most queries, in some cases by 2 orders of magnitude.


title: An integrated approach of diet and exercise recommendations for diabetes patients

abstract: Diabetes is among one of the fastest growing disease all over the world. Controlled diet and proper exercise are considered as a treatment to control diabetes. However, food and exercise suggestions in existing solutions do not consider integrated knowledge from personal profile, preferences, current vital signs, diabetes domain, food domain and exercise domain. Furthermore, there is a strong correlation of diet and exercise. We have implemented an ontology based integrated approach to combine knowledge from various domains to generate diet and exercise suggestions for diabetics. The solution is developed as a Semantic Healthcare Assistant for Diet and Exercise (SHADE). For each domain (person, diabetes, food and exercise) we have defined separate ontology along with rules and then an integrated ontology combines these individual ontologies. Finally, diet recommendations are presented in the form of various alternative menus such that each menu is a healthy and balanced diet.


title: Attachments beyond infancy.

abstract: Attachment theory is extended to pertain to developmental changes in the nature of children's attachments to parents and surrogate figures during the years beyond infancy, and to the nature of other affectional bonds throughout the life cycle. Various types of affectional bonds are examined in terms of the behavioral systems characteristic of each and the ways in which these systems interact. Specifically, the following are discussed: (a) the caregiving system that underlies parents' bonds to their children, and a comparison of these bonds with children's attachments to their parents; (b) sexual pair-bonds and their basic components entailing the reproductive, attachment, and caregiving systems; (c) friendships both in childhood and adulthood, the behavioral systems underlying them, and under what circumstances they may become enduring bonds; and (d) kinship bonds (other than those linking parents and their children) and why they may be especially enduring.


title: Balance of power: dynamic thermal management for Internet data centers

abstract: Internet-based applications and their resulting multitier distributed architectures have changed the focus of design for large-scale Internet computing. Internet server applications execute in a horizontally scalable topology across hundreds or thousands of commodity servers in Internet data centers. Increasing scale and power density significantly impacts the data center's thermal properties. Effective thermal management is essential to the robustness of mission-critical applications. Internet service architectures can address multisystem resource management as well as thermal management within data centers.


title: Snippets: Taking the High Road to a Low Level

abstract: When building a compiler for a high-level language, certain intrinsic features of the language must be expressed in terms of the resulting low-level operations. Complex features are often expressed by explicitly weaving together bits of low-level IR, a process that is tedious, error prone, difficult to read, difficult to reason about, and machine dependent. In the Graal compiler for Java, we take a different approach: we use snippets of Java code to express semantics in a high-level, architecture-independent way. Two important restrictions make snippets feasible in practice: they are compiler specific, and they are explicitly prepared and specialized. Snippets make Graal simpler and more portable while still capable of generating machine code that can compete with other compilers of the Java HotSpot VM.


title: Forgiveness and Health: Psycho-spiritual Integration and the Promotion of Better Healthcare

abstract: Psychology and religiousness/spirituality continue to be perceived as incongruent and incompatible, often resulting in a disconnection and suboptimal level of collaboration between the two fields to the detriment of healthcare. Nevertheless, forgiveness, or an absence of ill will, is a construct central to both mainstream world religion/spirituality and the field of psychology. Understanding and recognizing the construct of forgiveness and its mutually central application can foster increased collaboration between the fields. As a result, individually and collectively, the two fields will be better able to expand and further develop their many shared principles in the service of better healthcare.


title: How Leaders Self-Regulate Their Task Performance: Evidence that Power Promotes Diligence, Depletion, and Disdain

abstract: When leaders perform solitary tasks, do they self-regulate to maximize their effort, or do they reduce effort and conserve their resources? Our model suggests that power motivates self-regulation toward effective performance—unless the task is perceived as unworthy of leaders. Our 1st studies showed that power improves self-regulation and performance, even when resources for self-regulation are low (ego depletion). Additional studies showed that leaders sometimes disdain tasks they deem unworthy, by withholding effort (and therefore performing poorly). Ironically, during ego depletion, leaders skip the appraisal and, therefore, work hard regardless of task suitability, so that depleted leaders sometimes outperform nondepleted ones. Our final studies replicated these patterns with different tasks and even with simple manipulation of framing and perception of the same task (Experiment 5). Experiment 4 also showed that the continued high exertion of leaders when depleted takes a heavy toll, resulting in larger impairments later. The judicious expenditure of self-control resources among powerful people may help them prioritize their efforts to pursue their goals effectively.


title: A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation

abstract: In this letter, we present a simple, yet effective wavelet-based algorithm for estimating both global and local image sharpness (FISH, Fast Image Sharpness). FISH operates by first decomposing the input image via a three-level separable discrete wavelet transform (DWT). Next, the log-energies of the DWT subbands are computed. Finally, a scalar index corresponding to the image's overall sharpness is computed via a weighted average of these log-energies. Testing on several image databases demonstrates that, despite its simplicity, FISH is competitive with the currently best-performing techniques both for sharpness estimation and for no-reference image quality assessment.


title: Low-Voltage, Low-Power, and Wide-Tuning-Range Ring-VCO for Frequency ΔΣ Modulator

abstract: A low-voltage, low-power, and wide-tuning-range VCO which converts an analog input voltage to phase information for a frequency ΔΣ modulator is proposed in this paper. The VCO is based on a differential ring oscillator, which is improved with modified symmetric load and a positive feedback in the differential delay cells, a new bias circuit and a full-swing amplifier. The proposed VCO operating with two stages at a power supply voltage of 0.6 V can achieve wide tuning-range and low power consumption of 176.892 uW. The new VCO has a good linearity reducing harmonic distortion for frequency ΔΣ modulator. The circuit is designed using a 65 nm CMOS process.


title: Visualizing Linked Data with JavaScript

abstract: Despite the wealth of information contained in the Web of Linked Data, the current limitations and entry barriers of the Semantic Web technologies hinder the users from taking advantage of these information resources. Linked Data visualization can alleviate this problem. In this paper, we adopt a proper Linked Data visualization model, design Linked Data visualization algorithms, and develop a lightweight, easy-to-use prototype tool, LOD Viewer, using the platform independent JavaScript language. LOD Viewer can visualize different sources of RDF data including SPARQL endpoints for Linked Open Data (LOD) sources, and display the data in different graphic illustrations. Our case studies have verified the effectiveness and realizability of the proposed method. The time complexity analysis and experimental test show that the run-time of the proposed algorithms approximately exhibits a linear growth rate as the visualized RDF triples size increases.


title: Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning

abstract: We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training an order of magnitude faster. We scale Deep Voice 3 to dataset sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on a single GPU server.


title: Rfam 12.0: updates to the RNA families database

abstract: The Rfam database (available at http://rfam.xfam.org) is a collection of non-coding RNA families represented by manually curated sequence alignments, consensus secondary structures and annotation gathered from corresponding Wikipedia, taxonomy and ontology resources. In this article, we detail updates and improvements to the Rfam data and website for the Rfam 12.0 release. We describe the upgrade of our search pipeline to use Infernal 1.1 and demonstrate its improved homology detection ability by comparison with the previous version. The new pipeline is easier for users to apply to their own data sets, and we illustrate its ability to annotate RNAs in genomic and metagenomic data sets of various sizes. Rfam has been expanded to include 260 new families, including the well-studied large subunit ribosomal RNA family, and for the first time includes information on short sequence- and structure-based RNA motifs present within families.


title: Off-line robot palletizing simulator using optimized pattern and trajectory generation algorithm

abstract: Collision avoidance and robot path planning problems have emerged as a potential domain of robotics research of late because of their indispensable requirements in the field of manufacturing vis-a-vis material handling, such as picking and placing an object and loading/unloading a component to/from a machine or storage bins. This chapter focuses on palletization, a form of unitization in which a uniform load is stacked on a wooden pallet using a predetermined case pattern sequence and a given number of layers. In many kinds of proposed C-space construction approaches, several algorithms deal with the boundary of the C-obstacle analytically. Lozano Perez proposed the fundamentals of the C-space approach. When both the robot and obstacles have the shape of convex polygons, the Cobstacle boundary for an n-DOF manipulator is approximated by sets of n-1 dimensional slice planes, which are made from a one-dimensional slice plane. C. Zhao and his colleague proposed an algorithm to describe the C-obstacle as a set of parametric equations formulated from the mapping of the boundaries of the obstacles in a workspace. They use inverse pseudo kinematics to convert the obstacles in a workspace into a C-space. Debanik Roy studied path planning algorithms and their heuristics using the concept of visibility graph, and he presented an overview of the case study of robot path planning in an industrial environment in real time. Xiaojun and his colleague proposed a two-phase approach for C-obstacle construction and the collision detection of manipulators. This method is applicable to manipulators with various types of kinematic structures and geometric shapes. M. Pettersson and his coworkers proposed trajectory optimization method considering fatigue and thermal load of real robot. They also referred that the proposed method could be directly adapted to palletizing system. The issues of these papers, however, are for the operation of real industrial robot, and it is a part of entire palletizing system. Many other latest researches solely oriented towards path planning or modified apparatuses to improve the specified handling task have also been conducted. Studies on the total robot palletizing system, however, which integrates loading pattern optimization, robot OLP simulation, and path optimization, have yet to be systematically conducted. This study was dedicated to the development of OLP Simulation S/W for a robot palletizing system (Non-vision system), which means that this study prioritized the reflection of the 17


title: Data-Dependent Stability of Stochastic Gradient Descent

abstract: We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to previous distribution-free algorithmic stability results for SGD which depend on the worst-case constants. By virtue of the data-dependent argument, our bounds provide new insights into learning with SGD on convex and non-convex problems. In the convex case, we show that the bound on the generalization error depends on the risk at the initialization point. In the non-convex case, we prove that the expected curvature of the objective function around the initialization point has crucial influence on the generalization error. In both cases, our results suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization. As a corollary, our results allow us to show optimistic generalization bounds that exhibit fast convergence rates for SGD subject to a vanishing empirical risk and low noise of stochastic gradient.


title: Invertible grayscale

abstract: Once a color image is converted to grayscale, it is a common belief that the original color cannot be fully restored, even with the state-of-the-art colorization methods. In this paper, we propose an innovative method to synthesize invertible grayscale. It is a grayscale image that can fully restore its original color. The key idea here is to encode the original color information into the synthesized grayscale, in a way that users cannot recognize any anomalies. We propose to learn and embed the color-encoding scheme via a convolutional neural network (CNN). It consists of an encoding network to convert a color image to grayscale, and a decoding network to invert the grayscale to color. We then design a loss function to ensure the trained network possesses three required properties: (a) color invertibility, (b) grayscale conformity, and (c) resistance to quantization error. We have conducted intensive quantitative experiments and user studies over a large amount of color images to validate the proposed method. Regardless of the genre and content of the color input, convincing results are obtained in all cases.


title: Learning Inference Models for Computer Vision

abstract: Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques. This thesis proposes novel inference schemes and demonstrates applications in computer vision. We propose inference techniques for both generative and discriminative vision models. The use of generative models in vision is often hampered by the difficulty of posterior inference. We propose techniques for improving inference in MCMC sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge. We concentrate on CNN models and propose a generalization of standard spatial convolutions to bilateral convolutions. We generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call `Bilateral Neural Networks'. Experiments demonstrate the use of the bilateral networks on a wide range of image and video tasks and datasets. In summary, we propose techniques for better inference in several vision models ranging from inverse graphics to freely parameterized neural networks. In generative models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way to incorporate prior knowledge into CNNs.


title: Methods of state estimation resilient against sensor attacks and robust against exogenous disturbances

abstract: Control systems that are resilient against malicious attacks have recently become one of the important research topics. This is due to the fact that many critical infrastructures in our society such as power grid, nuclear facility, and public transportation, rely on feedback control technology, and that malfunction of said systems due to attack could be disastrous. Malicious attacks on control systems indeed occurred in recent years. The problem of resilient state estimation against sensor attacks, which is concerned of correctly estimating plant state despite of sensor attacks, have been previously addressed. Existing methods, however, lose the accuracy of the estimation if external disturbance exists. In this paper, we investigate the problem of resilient state estimation that are robust against external disturbance. Specifically, unknown input observer based state estimation has been developed to eliminate the effect of external disturbance on the state estimation that are resilient against malicious attacks on sensors. Associated design conditions have been derived. Additionally, disturbance observer based state estimation method has been developed. Validation of the proposed methods is provided through experiments.


title: Mindfulness practice leads to increases in regional brain gray matter density

abstract: article i nfo Therapeutic interventions that incorporate training in mindfulness meditation have become increasingly popular, but to date little is known about neural mechanisms associated with these interventions. Mindfulness-Based Stress Reduction (MBSR), one of the most widely used mindfulness training programs, has been reported to produce positive effects on psychological well-being and to ameliorate symptoms of a number of disorders. Here, we report a controlled longitudinal study to investigate pre-post changes in brain gray matter concentration attributable to participation in an MBSR program. Anatomical magnetic resonance (MR) images from 16 healthy, meditation-naive participants were obtained before and after they underwent the 8-week program. Changes in gray matter concentration were investigated using voxel-based morphometry, and compared with a waiting list control group of 17 individuals. Analyses in a priori regions of interest confirmed increases in gray matter concentration within the left hippocampus. Whole brain analyses identified increases in the posterior cingulate cortex, the temporo-parietal junction, and the cerebellum in the MBSR group compared with the controls. The results suggest that participation in MBSR is associated with changes in gray matter concentration in brain regions involved in learning and memory processes, emotion regulation, self-referential processing, and perspective taking.


title: Mechanisms of motivation–cognition interaction: challenges and opportunities

abstract: Recent years have seen a rejuvenation of interest in studies of motivation–cognition interactions arising from many different areas of psychology and neuroscience. The present issue of Cognitive, Affective, & Behavioral Neuroscience provides a sampling of some of the latest research from a number of these different areas. In this introductory article, we provide an overview of the current state of the field, in terms of key research developments and candidate neural mechanisms receiving focused investigation as potential sources of motivation–cognition interaction. However, our primary goal is conceptual: to highlight the distinct perspectives taken by different research areas, in terms of how motivation is defined, the relevant dimensions and dissociations that are emphasized, and the theoretical questions being targeted. Together, these distinctions present both challenges and opportunities for efforts aiming toward a more unified and cross-disciplinary approach. We identify a set of pressing research questions calling for this sort of cross-disciplinary approach, with the explicit goal of encouraging integrative and collaborative investigations directed toward them.


title: Understanding Natural Language Queries over Relational Databases

abstract: Natural language has been the holy grail of query interface designers, but has generally been considered too hard to work with, except in limited specific circumstances. In this paper, we describe the architecture of an interactive natural language query interface for relational databases. Through a carefully limited interaction with the user, we are able to correctly interpret complex natural language queries, in a generic manner across a range of domains. By these means, a logically complex English language sentence is correctly translated into a SQL query, which may include aggregation, nesting, and various types of joins, among other things, and can be evaluated against an RDBMS.We have constructed a\ system, NaLIR (Natural Language Interface for Relational databases), embodying these ideas. Our experimental assessment, through user studies, demonstrates that NaLIR is good enough to be usable in practice: even naive users are able to specify quite complex ad-hoc queries.


title: 2002) Measuring the intrusiveness of advertisements: Scale development and validation

abstract: Abstract The current study examines consumers'perceptions of the intrusiveness of advertisements. A scale is developed to tap an underlying construct that has not previously been measured. Following traditional methods of scale development, the study uses expert-generated adjective lists, expands possible measures using a thesaurus, and finally reduces the number of items statistically to derive a new measure of advertising intrusiveness. The scale is validated using samples in different experimental conditions and is found to be valid, reliable, and parsimonious. The importance of such a scale for the field of advertising is discussed.


title: Measuring the health of open source software ecosystems: Beyond the scope of project health

abstract: Abstract Background The livelihood of an open source ecosystem is important to different ecosystem participants: software developers, end-users, investors, and participants want to know whether their ecosystem is healthy and performing well. Currently, there exists no working operationalization available that can be used to determine the health of open source ecosystems. Health is typically looked at from a project scope, not from an ecosystem scope. Objectives With such an operationalization, stakeholders can make better decisions on whether to invest in an ecosystem: developers can select the healthiest ecosystem to join, keystone organizers can establish which governance techniques are effective, and end-users can select ecosystems that are robust, will live long, and prosper. Method Design research is used to create the health operationalization. The evaluation step is done using four ecosystem health projects from literature. Results The Open Source Ecosystem Health Operationalization is provided, which establishes the health of a complete software ecosystem, using the data from collections of open source projects that belong to the ecosystem. Conclusion The groundwork is done, by providing a summary of research challenges, for more research in ecosystem health. With the operationalization in hand, researchers no longer need to start from scratch when researching open source ecosystems’ health.


title: Automatic Generation of Text Descriptive Comments for Code Blocks

abstract: We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code-RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.


title: Fronthauling for 5G LTE-U Ultra Dense Cloud Small Cell Networks

abstract: Ultra dense cloud small cell network (UDCSNet), which combines cloud computing and massive deployment of small cells, is a promising technology for 5G LTE-U mobile communications because it can accommodate the anticipated explosive growth of mobile users’ data traffic. As a result, fronthauling becomes a challenging problem in 5G LTE-U UDCSNet. In this article, we present an overview of the challenges and requirements of the fronthaul technology in 5G LTE-U UDCSNets. We survey the advantages and challenges for various candidate fronthaul technologies such as optical fiber, millimeter- wave-based unlicensed spectrum, Wi-Fibased unlicensed spectrum, sub-6-GHz-based licensed spectrum, and free-space optical-based unlicensed spectrum.


title: Realtime projective multi-texturing of pointclouds and meshes for a realistic street-view web navigation

abstract: Street-view web applications have now gained widespread popularity. Targeting the general public, they offer ease of use, but while they allow efficient navigation from a pedestrian level, the immersive quality of such renderings is still low. The user is usually stuck at specific positions and transitions bring out artefacts, in particular parallax and aliasing. We propose a method to enhance the realism of street view navigation systems using a hybrid rendering based on realtime projective texturing on meshes and pointclouds with occlusion handling, requiring extremely minimized pre-processing steps allowing fast data update, progressive streaming (mesh-based approximation, with point cloud details) and unaltered raw data precise visualization.


title: Understanding Smart Cities: An Integrative Framework

abstract: Making a city "smart" is emerging as a strategy to mitigate the problems generated by the urban population growth and rapid urbanization. Yet little academic research has sparingly discussed the phenomenon. To close the gap in the literature about smart cities and in response to the increasing use of the concept, this paper proposes a framework to understand the concept of smart cities. Based on the exploration of a wide and extensive array of literature from various disciplinary areas we identify eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. These factors form the basis of an integrative framework that can be used to examine how local governments are envisioning smart city initiatives. The framework suggests directions and agendas for smart city research and outlines practical implications for government professionals.


title: Load-independent Class EF inverters for inductive wireless power transfer

abstract: This paper will present the modelling, analysis and design of a load-independent Class EF inverter. This inverter is able to maintain zero-voltage switching (ZVS) operation and produce a constant output current for any load value without the need for tuning or replacement of components. The load-independent feature of this inverter is beneficial when used as the primary coil driver in multi-megahertz high power inductive wireless power transfer (WPT) applications where the distance between the coils and the load are variable. The work here begins with the traditional load-dependent Class EF topology for inversion and then specifies the criteria that are required to be met in order achieve load-independence. The design and development of a 240W load-independent Class EF inverter to drive the primary coil of a 6.78MHz WPT system will be discussed and experimental results will be presented to show the load-independence feature when the distance between the coils of the WPT system changes.


title: The use of EUS to diagnose malignant portal venous system invasion by pancreatic cancer.

abstract: BACKGROUND ::: CT scanning and mesenteric angiography are insensitive tests diagnosing vascular invasion by pancreatic cancer. Endoscopic ultrasound (EUS) has been proposed as an alternative. The sensitivity, specificity, and accuracy of specific EUS criteria for diagnosing malignant invasion of the branches of the portal venous system have not been determined. ::: ::: ::: METHODS ::: This is a prospective blinded evaluation of EUS and angiography to diagnose malignant invasion of the portal venous system by pancreatic cancer in 45 patients, 28 of whom underwent surgery. Surgical staging was used as the gold standard for determining the accuracy of EUS and angiography. ::: ::: ::: RESULTS ::: Four EUS criteria were studied and the overall accuracy rates were as follows: irregular venous wall (87%), loss of interface (78%), proximity of mass (73%), and size (39%). Although "irregular venous wall" was the most accurate, it suffered from a low sensitivity rate (47%) because of its relative inability to detect superior mesenteric vein invasion (sensitivity of 17%). The angiographic criteria had accuracy rates of 73% to 90% with low sensitivity rates (20% to 77%). The clean resection rate was 86% when all tests were used, 78% if EUS was used without angiography, and 60% if only angiography was used. ::: ::: ::: CONCLUSION ::: EUS is highly sensitive for detecting portal and splenic vein invasion by pancreatic cancer, but may be insensitive for superior mesenteric vein involvement.


title: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach

abstract: This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used by each node to keep accurate statistics on which routing decisions lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, Q-routing proves superior to a nonadaptive algorithm based on precomputed shortest paths and is able to route efficiently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically. The paper concludes with a discussion of the tradeoff between discovering shortcuts and maintaining stable policies.


title: Design and experimental evaluation of a 2.4 GHz-AoA-enhanced beamsteering algorithm for IEEE 802.11ad mm-wave WLANs

abstract: IEEE 802.11ad millimeter-wave (mm-wave) WLAN nodes achieve high throughput at the cost of the frequent re-steering requirement of highly directional antenna beams to establish and maintain links disrupted by beam misalignment, node mobility, or link blockage. Consequently, rapid and robust beamsteering algorithms are essential to enable seamless communication in mm-wave WLANs. In this paper we propose AoASteer, a beamsteering algorithm that speeds up the link establishment process in IEEE 802.11ad mm-wave WLANs by preferentially searching over a subset of mm-wave antenna sectors predicted by 2.4 GHz angle of arrival (AoA) estimation at the access point (AP). We experimentally evaluate the performance of AoASteer through extensive measurements in several indoor and outdoor locations, using 60 GHz USRP-SiversIMA packet radio transceivers to gather real mm-wave link information, and a 2.4 GHz USRP-based receiver with an 8-element uniform linear antenna array for AoA estimation, both implemented using GNU Radio. Our evaluation results, obtained for APs with different numbers of beams per sector, show that AoASteer typically selects a near-optimal LOS or NLOS link to establish communication, while significantly reducing the link establishment latency. For example, for 4-sector mm-wave antennas, AoASteer reduces the latency by 46 μs for 73% of our measurement cases compared to the IEEE 802.11ad beamsteering algorithm, while achieving the highest data rate of 6.7 Gbps for 92% of the cases.


title: Context-Aware wireless sensor networks for assisted-living and residential monitoring

abstract: Improving the quality of healthcare and the prospects of "aging in place" using wireless sensor technology requires solving difficult problems in scale, energy management, data access, security, and privacy. We present AlarmNet, a novel system for assisted living and residential monitoring that uses a two-way flow of data and analysis between the front- and back-ends to enable context-aware protocols that are tailored to residents' individual patterns of living. AlarmNet integrates environmental, physiological, and activity sensors in a scalable heterogeneous architecture. The SenQ query protocol provides real-time access to data and lightweight in-network processing. Circadian activity rhythm analysis learns resident activity patterns and feeds them back into the network to aid context-aware power management and dynamic privacy policies.


title: Probabilistic Data Analysis with Probabilistic Programming

abstract: Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.


title: Special issue article When bittersweet turns sour: Adverse effects of nostalgia on habitual worriers

abstract: Nostalgic memories can be pleasant, albeit bittersweet, and have been found beneficial for well-being. This study demonstrated that for individuals who habitually worry, nostalgia may not be such a nourishing experience. Nostalgia was experimentally induced using a visual imagery task and resulted in positive affect. Although this was also the case for participants who habitually worry, these individuals subsequently showed more signs of anxiety and depression than habitual worriers in a control condition. The findings fit within a control theoretical perspective; as habitual worriers’ actual chronic state of anxiety contrasts with nostalgic memoriesofa carefreepast,thismay instigatefurtherruminationleadingtodistress. Amorepresent-orientedtimeperspective,such as mindfulness, is discussed as being beneficial for habitual worriers. Copyright © 2012 John Wiley & Sons, Ltd.


title: Toward automated segmentation of the pathological lung in CT

abstract: Conventional methods of lung segmentation rely on a large gray value contrast between lung fields and surrounding tissues. These methods fail on scans with lungs that contain dense pathologies, and such scans occur frequently in clinical practice. We propose a segmentation-by-registration scheme in which a scan with normal lungs is elastically registered to a scan containing pathology. When the resulting transformation is applied to a mask of the normal lungs, a segmentation is found for the pathological lungs. As a mask of the normal lungs, a probabilistic segmentation built up out of the segmentations of 15 registered normal scans is used. To refine the segmentation, voxel classification is applied to a certain volume around the borders of the transformed probabilistic mask. Performance of this scheme is compared to that of three other algorithms: a conventional, a user-interactive and a voxel classification method. The algorithms are tested on 10 three-dimensional thin-slice computed tomography volumes containing high-density pathology. The resulting segmentations are evaluated by comparing them to manual segmentations in terms of volumetric overlap and border positioning measures. The conventional and user-interactive methods that start off with thresholding techniques fail to segment the pathologies and are outperformed by both voxel classification and the refined segmentation-by-registration. The refined registration scheme enjoys the additional benefit that it does not require pathological (hand-segmented) training data.


title: A TDC-Less 7 mW 2.5 Gb/s Digital CDR With Linear Loop Dynamics and Offset-Free Data Recovery

abstract: A digital clock and data recovery circuit (CDR) employs hybrid analog/digital phase detection to achieve linear loop dynamics and to eliminate the nonlinearity and quantization error of a bang-bang phase detector. The proposed architecture achieves constant jitter transfer bandwidth independent of input data jitter and reduces the sensitivity to digitally-controlled oscillator's frequency quantization error and consecutive identical digits. The hybrid phase detection scheme also helps decouple jitter generation from jitter transfer characteristics of the CDR. The proto-type digital CDR fabricated in 0.13 μm CMOS technology achieves error-free operation (BER <; 10-12) for PRBS data sequences ranging from 27 - 1 to 231-1 sequence lengths over 0.5 Gb/s to 3.2 Gb/s data rates. At 2.5 Gb/s, the CDR consumes 7 mW power from a single 1.2 V supply and the recovered clock jitter is 5.7 ps rms.


title: Curved folding

abstract: Fascinating and elegant shapes may be folded from a single planar sheet of material without stretching, tearing or cutting, if one incorporates curved folds into the design. We present an optimization-based computational framework for design and digital reconstruction of surfaces which can be produced by curved folding. Our work not only contributes to applications in architecture and industrial design, but it also provides a new way to study the complex and largely unexplored phenomena arising in curved folding.


title: A Novel Single-Feed Circular Microstrip Antenna With Reconfigurable Polarization Capability

abstract: A single-feed circular microstrip antenna with reconfigurable polarization capability is proposed. This antenna has a simple structure, which consists of a radiating circular patch, five switches (PIN diode), three matching stubs, and a 50 microstrip feed line. It can be switched between 4 different states: two states (low-frequency and high-frequency) for linear polarization (LP), one state for left hand circular polarization (LHCP) and one for right hand circular polarization (RHCP) by controlling the bias voltage of two PIN diodes. At the same time, three switchable matching stubs are used for matching every polarization state. Simulation results and experimental results show that the proposed antenna demonstrates a low cross polarization level, good impedance bandwidth, and a very good axial ratio in the circularly polarized states.


Brand Attitude Strength : Conceptual and Empirical Differentiation ofTwo Critical Brand Equity Drivers