title: Control And Kinematic Design Of Multi-Degree-Of-Freedom Mobile Robots With Compliant Linkage

abstract: Mobile robots (unlike automated guided vehicles) usually rely on dead-reckoning between periodic absolute position updates and their performance is diminished by excessive wheel slippage. This paper introduces compliant linkage, a new concept in the control and kinematic design of multi-degree-of-freedom (MDOF) mobile robots. As the name implies, compliant linkage provides compliance between the drive wheels or drive axles of a vehicle, to accommodate control errors which would otherwise cause wheel slippage. The concept of compliant linkage was implemented and tested on a 4-DOF vehicle built at the University of Michigan's Mobile Robotics Lab. Experimental results show that control errors are effectively absorbed by the linkage, resulting in smooth and precise motion. The dead-reckoning accuracy of the compliant linkage vehicle is substantially better than that reported in the literature for other MDOF vehicles; it was found equal to, or even better than that of comparable 2-DOF vehicles. >


title: Determination of water depth with high-resolution satellite imagery over variable bottom types

abstract: A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms—the standard linear transform and the new ratio transform— were compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10‐15 m. However, the linear transform does not distinguish depths .15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in .25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4‐5 pixels) in water depths .15‐20 m. In general, the ratio transform is more robust than the linear transform.


title: Pervasive games: bringing computer entertainment back to the real world

abstract: This article gives an introduction and overview of the field of pervasive gaming, an emerging genre in which traditional, real-world games are augmented with computing functionality, or, depending on the perspective, purely virtual computer entertainment is brought back to the real world.The field of pervasive games is diverse in the approaches and technologies used to create new and exciting gaming experiences that profit by the blend of real and virtual game elements. We explicitly look at the pervasive gaming sub-genres of smart toys, affective games, tabletop games, location-aware games, and augmented reality games, and discuss them in terms of their benefits and critical issues, as well as the relevant technology base.


title: Multimodal Spam Classification Using Deep Learning Techniques

abstract: The internet has been beneficial to the society in ways more than one, the power to learn anything anywhere, the power to always be connected to the people you love. But as usual, there are two sides to coin. The E-mail system has been the backbone for communication between professionals for a very long time, but it is plagued by the unwanted influence of spam. In this paper we classify a mail into spam or not-spam (ham) by analyzing the whole content i.e. Image and Text, processing it through independent classifiers using Convolutional Neural Networks. We finally propose two hybrid multi-modal architectures by forging the image and text classifiers. Our experimental results outperform the current state-of-the-art methods and provide a new baseline for future research in the field


title: IEEE 1451-1-6: Providing common network services over MQTT

abstract: IEEE 1451 has been around for almost 20 years and in that time it has seen many changes in the world of smart sensors. One of the most distinct paradigms to arise was the Internet-of-Things and with it, the popularity of light-weight and simple to implement communication protocols. One of these protocols in particular, MQ Telemetry Transport has become synonymous with large cloud service providers such as Amazon Web Services, IBM Watson, and Microsoft Azure, along with countless other services. While MQTT had be traditionally used in controlled networks within server centers, the simplicity of the protocol has caused it to be utilized on the open internet. Now being called the language of the IoT, it seems obvious that any standard that is aiming to bring a common network service layer to the IoT architecture should be able to utilize MQTT. This paper proposes potential methodologies to extend the Common Architectures and Network services found in the IEEE 1451 Family of Standard into applications which utilize MQTT.


title: A software product line life cycle cost estimation model

abstract: Most software product line cost estimation models are calibrated only to local product line data rather than to a broad range of product lines. They also underestimate the return on investment for product lines by focusing only on development vs. life-cycle savings, and by applying writing-for-reuse surcharges to the entire product rather that to the portions of the product being reused. This paper offers some insights based on the exploratory development and collaborative refinement of a software product line life cycle economics model, the Constructive Product Line Investment Model (COPLIMO) that addresses these shortfalls. COPLIMO consists of two components: a product line development cost model and an annualized post-development life cycle extension. It focuses on modeling the portions of the software that involve product-specific newly-built software, fully reused black-box product line components, and product line components that are reused with adaptation. This model is an extension built upon USC-CSE's well-calibrated, multi-parameter Constructive Cost Model (COCOMO) II, tailored down to cover the essentials of strategic software product line decision issues and available supporting data from industries.


A Generic Entity-Relationship Model


title: Evaluating visual aesthetics in photographic portraiture

abstract: We propose and demonstrate a strategy to quantify aesthetic quality in photographs. Our approach is to develop a small set of classification features by tuning general compositional principles to a targeted image domain where saliency can be better understood. We demonstrate this strategy with photographic portraits of individuals, but it can be extended to other domains. Our technique leverages a refined method of using templates as spatial composition feature look-up tables. Compared to the traditional approach using a large set of global and local features extracted with little salient knowledge, classifiers using features extracted with our approach are better predictors of human aesthetic judgments.


title: MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0

abstract: We announce the release of the fourth version of MEGA software, which expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written in figure legend format, in order to provide natural language descriptions of the models and methods used in the analyses. This facility aims to promote a better understanding of the underlying assumptions used in analyses, and of the results generated. Another new feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary distances between all pairs of sequences simultaneously, with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages. This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree. This new version is a native 32-bit Windows application with multi-threading and multi-user supports, and it is also available to run in a Linux desktop environment (via the Wine compatibility layer) and on Intel-based Macintosh computers under the Parallels program. The current version of MEGA is available free of charge at (http://www.megasoftware.net).


title: Changing perspectives in the prevention of coronary artery disease.

abstract: Today the question is no longer whether cholesterol reduction is beneficial for those at risk for coronary artery disease; the questions now are when, whom and how to treat. Areas of great interest include extrapolation of current trial results to low density lipoprotein reduction by diet and drugs, and assessment of the value of increasing high density lipoprotein levels by pharmacologic means. We will need to decide what measurements (total cholesterol, lipoprotein cholesterol or lipoprotein apoprotein levels) are of most value to the diagnosis, treatment and follow-up of the at-risk patient. Recommendations, including those of the recently published National Institutes of Health Consensus Panel on Cholesterol Lowering, suggest that our index for diagnosis and treatment should be set considerably lower than it is today. To be successful with a more aggressive approach to cholesterol lowering, we will need to better support, educate and motivate the at-risk patient. Physicians need to become more knowledgeable about what plasma cholesterol is and how to change it. Methods that enhance patient adherence to diet and drug therapy must be developed. We will need to alter lifetime habits and will need the help of both the food industry and better informed consumers, knowledgeable on how to read food labels, if we are to succeed. Ultimately, we will need a 2-pronged approach, focusing on both the physician and the public at large.


title: Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers

abstract: Sentiment Analysis has seen much progress in the past two decades. For the past few years, neural network approaches, primarily RNNs and CNNs, have been the most successful for this task. Recently, a new category of neural networks, self-attention networks (SANs), have been created which utilizes the attention mechanism as the basic building block. Self-attention networks have been shown to be effective for sequence modeling tasks, while having no recurrence or convolutions. In this work we explore the effectiveness of the SANs for sentiment analysis. We demonstrate that SANs are superior in performance to their RNN and CNN counterparts by comparing their classification accuracy on six datasets as well as their model characteristics such as training speed and memory consumption. Finally, we explore the effects of various SAN modifications such as multi-head attention as well as two methods of incorporating sequence position information into SANs.


title: Time varying undirected graphs

abstract: Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using $\ell_1$ penalization methods. However, current methods assume that the data are independent and identically distributed. If the distribution, and hence the graph, evolves over time then the data are not longer identically distributed. In this paper, we show how to estimate the sequence of graphs for non-identically distributed data, where the distribution evolves over time.


title: Authenticated Append-only Skip Lists

abstract: In this work we describe, design and analyze the security of a tamper-evident, append-only data structure for maintaining secure data sequences in a loosely coupled distributed system where individual system components may be mutually distrustful. The resulting data structure, called an Authenticated Append-Only Skip List (AASL), allows its maintainers to produce one-way digests of the entire data sequence, which they can publish to others as a commitment on the contents and order of the sequence. The maintainer can produce efficiently succinct proofs that authenticate a particular datum in a particular position of the data sequence against a published digest. AASLs are secure against tampering even by malicious data structure maintainers. First, we show that a maintainer cannot ``invent'' and authenticate data elements for the AASL after he has committed to the structure. Second, he cannot equivocate by being able to prove conflicting facts about a particular position of the data sequence. This is the case even when the data sequence grows with time and its maintainer publishes successive commitments at times of his own choosing. ::: AASLs can be invaluable in reasoning about the integrity of system logs maintained by untrusted components of a loosely-coupled distributed system.


title: Asymmetry-aware link quality services in wireless sensor networks

abstract: Recent studies in wireless sensor networks (WSN) have observed that the irregular link quality is a common phenomenon, rather than an anomaly. The irregular link quality, especially link asymmetry, has significant impacts on the design of WSN protocols. In this paper, we propose two asymmetry-aware link quality services: the neighborhood link quality service (NLQS) and the link relay service (LRS). The novelty of the NLQS service is taking the link asymmetry into consideration to provide timeliness link quality and distinguishing the inbound and outbound neighbors with the support of LRS, which builds a relay framework to alleviate the effects of link asymmetry. To demonstrate the proposed link quality services, we design and implement two example applications, the shortest hops routing tree (SHRT) and the best path reliability routing tree (BRRT), on the TinyOS platform. We found that the performance of two example applications is improved substantially. More than 40% of nodes identify more outbound neighbors and the percentage of increased outbound neighbors is between 14% and 100%. In SHRT, more than 15% of nodes reduce hops of the routing tree and the percentage of reduced hops is between 14% and 100%. In BRRT, more than 16% of nodes improve the path reliability of the routing tree and the percentage of the improved path reliability is between 2% to 50%.


title: Genetic Algorithm Based Path Planning and Optimization for Autonomous Mobile Robots with Morphological Preprocessing

abstract: This paper presents an algorithm for optimal path planning for mobile robots using Genetic Algorithms coupled with morphological image preprocessing of the terrain. Path Planning in a given environment, being a NP-Hard problem, is computationally demanding especially if exact or deterministic techniques are employed. This paves the way for the use of evolutionary computing techniques, such as Genetic Algorithms (GAs), for the solution of this problem. However as GAs tend to find optimal paths without considering the location and distribution of obstacles in the given map, therefore a large amount of time may be spent before even a feasible path is found. The technique conferred in this paper overcomes this shortcoming by using specialized morphological preprocessing techniques applied on the landscape representative image. The results obtained on a variety of structured, unstructured and clustered terrains indicate the efficacy of the algorithm presented and the potential for the use of this algorithm in real-time operation of mobile robots.


title: Label-free, real-time and multiplex detection of Mycobacterium tuberculosis based on silicon photonic microring sensors and asymmetric isothermal amplification technique (SPMS-AIA)

abstract: Abstract Rapid diagnosis of Mycobacterium tuberculosis complex (MTBC) is crucial for the control and treatment of tuberculosis. Currently available diagnostic tools are unable to meet clinical needs, especially in resource limited settings, as they suffer from being time-consuming, costly, insensitive, or dependant on sophisticated instruments and well trained personnel. An optical diagnostic platform (SPMS-AIA) consisting of the silicon photonic microring sensor and the asymmetric isothermal amplification technique was developed to detect MTBC in a rapid (one hour), isothermal, label-free and real-time manner. The performance of the SPMS-AIA platform was assessed by detecting two MTBC specific biomarkers, IS 6110 and IS 1081 . Detection limits were as low as 3.2 copies for IS 6110 and 12 copies for IS1081 respectively when tested with Mycobacterium tuberculosis H37Rv genomic DNA. An evaluation for detection of IS 6110 in clinical sputa showed that the SPMS-AIA platform performed as well as the established market leader GeneXpert MTB/RIF platform. Of 26 MTBC culture-positive and smear-positive specimens, 23 were found positive by the SPMS-AIA platform (89% sensitivity). Of 47 MTBC culture-positive and smear-negative specimens, 34 were positive by the SPMS-AIA platform (72% sensitivity): this is similar to the sensitivity of 70% by the GeneXpert MTB/RIF platform. The versatility of the SPMS-AIA platform was demonstrated by multiplex detection of IS 6110 and IS 1081 in 10 clinical specimens. The SPMS-AIA platform shows great potential for development into a point-of-care (POC) diagnostic tool for use in resource limited settings.


title: A verified compiler for an impure functional language

abstract: We present a verified compiler to an idealized assembly language from a small, untyped functional language with mutable references and exceptions. The compiler is programmed in the Coq proof assistant and has a proof of total correctness with respect to big-step operational semantics for the source and target languages. Compilation is staged and includes standard phases like translation to continuation-passing style and closure conversion, as well as a common subexpression elimination optimization. In this work, our focus has been on discovering and using techniques that make our proofs easy to engineer and maintain. While most programming language work with proof assistants uses very manual proof styles, all of our proofs are implemented as adaptive programs in Coq's tactic language, making it possible to reuse proofs unchanged as new language features are added. In this paper, we focus especially on phases of compilation that rearrange the structure of syntax with nested variable binders. That aspect has been a key challenge area in past compiler verification projects, with much more effort expended in the statement and proof of binder-related lemmas than is found in standard pencil-and-paper proofs. We show how to exploit the representation technique of parametric higher-order abstract syntax to avoid the need to prove any of the usual lemmas about binder manipulation, often leading to proofs that are actually shorter than their pencil-and-paper analogues. Our strategy is based on a new approach to encoding operational semantics which delegates all concerns about substitution to the meta language, without using features incompatible with general-purpose type theories like Coq's logic.


Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration.


title: The Delayed D* Algorithm for Efficient Path Replanning

abstract: Mobile robots are often required to navigate environments for which prior maps are incomplete or inaccurate. In such cases, initial paths generated for the robots may need to be amended as new information is received that is in conflict with the original maps. The most widely used algorithm for performing this path replanning is Focussed Dynamic A* (D*), which is a generalization of A* for dynamic environments. D* has been shown to be up to two orders of magnitude faster than planning from scratch. In this paper, we present a new replanning algorithm that generates equivalent paths to D* while requiring about half its computation time. Like D*, our algorithm incrementally repairs previous paths and focusses these repairs towards the current robot position. However, it performs these repairs in a novel way that leads to improved efficiency.


title: A Fuzzy Restricted Boltzmann Machine: Novel Learning Algorithms Based on the Crisp Possibilistic Mean Value of Fuzzy Numbers

abstract: A fuzzy restricted Boltzmann machine (FRBM) is extended from a restricted Boltzmann machine (RBM) by replacing all the real-valued parameters with fuzzy numbers. A new FRBM that employs the crisp possibilistic mean value of a fuzzy number to defuzzify the fuzzy free energy function is presented. This approach is much clearer and easier to obtain the expression of the defuzzified free energy function and its approximation than the centroid method. Several theorems that discuss the error bounds of the approximation to ensure the rationality and validity are also investigated. Learning algorithms are given for the designed FRBM with symmetric triangular fuzzy numbers (STFNs), asymmetric triangular fuzzy numbers, and Gaussian fuzzy numbers. By appropriately choosing the parameters, a theorem is concluded that all FRBMs with symmetric fuzzy numbers will have identical learning algorithm to that of FRBMs with STFNs. This is illustrated by a case of FRBM with Gaussian fuzzy numbers. Two experiments including the MNIST handwriting recognition and the Bars-and-Stripes benchmark are carried out. The results show that the proposed FRBMs significantly outperform RBMs in learning accuracy and generalization ability, especially when encountering unlearned samples and recovering incomplete images.


title: Evaluation of a Behavioral Mobile Phone App Intervention for the Self-Management of Type 2 Diabetes: Randomized Controlled Trial Protocol

abstract: Background: Patients with type 2 diabetes mellitus (T2DM) struggle with the management of their condition due to difficulty relating lifestyle behaviors with glycemic control. While self-monitoring of blood glucose (SMBG) has proven to be effective for those treated with insulin, it has been shown to be less beneficial for those only treated with oral medications or lifestyle modification. We hypothesized that the effective self-management of non-insulin treated T2DM requires a behavioral intervention that empowers patients with the ability to self-monitor, understand the impact of lifestyle behaviors on glycemic control, and adjust their self-care based on contextualized SMBG data. Objective: The primary objective of this randomized controlled trial (RCT) is to determine the impact of bant2, an evidence-based, patient-centered, behavioral mobile app intervention, on the self-management of T2DM. Our second postulation is that automated feedback delivered through the mobile app will be as effective, less resource intensive, and more scalable than interventions involving additional health care provider feedback. Methods: This study is a 12-month, prospective, multicenter RCT in which 150 participants will be randomly assigned to one of two groups: the control group will receive current standard of care, and the intervention group will receive the mobile phone app system in addition to standard of care. The primary outcome measure is change in glycated hemoglobin A1c from baseline to 12 months. Results: The first patient was enrolled on July 28, 2015, and we anticipate completing this study by September, 2018. Conclusions: This RCT is one of the first to evaluate an evidence-based mobile app that focuses on facilitating lifestyle behavior change driven by contextualized and structured SMBG. The results of this trial will provide insights regarding the usage of mobile tools and consumer-grade devices for diabetes self-care, the economic model of using incentives to motivate behavior change, and the consumption of test strips when following a rigorously structured approach for SMBG. Trial Registration: ClinicalTrials.gov NCT02370719; https://clinicaltrials.gov/ct2/show/NCT02370719 (Archived at http://www.webcitation.org/6jpyjfVRs) [JMIR Res Protoc 2016;5(3):e174]


title: Navigation Strategies for Exploring Indoor Environments

abstract: In this paper, we investigate safe and efficient map-building strategies for a mobile robot with imperfect control and sensing. In the implementation, a robot equipped with a range sensor builds apolygonal map layout of a previously unknown indoor environment. The robot explores the environment and builds the map concurrently by patching together the local models acquired by the sensor into a global map. A well-studied and related problem is the simultaneous localization and mapping SLAM problem, where the goal is to integrate the information collected during navigation into the most accurate map possible. However, SLAM does not address the sensor-placement portion of the map-building task. That is, given the map built so far, where should the robot go next? This is the main question addressed in this paper. Concretely, an algorithm is proposed to guide the robot through a series of ?good? positions, where ?good? refers to the expected amount and quality of the information that will be revealed at each new location. This is similar to the next-best-view NBV problem studied in computer vision and graphics. However, in mobile robotics the problem is complicated by several issues, two of which are particularly crucial. One is to achieve safe navigation despite an incomplete knowledge of the environment and sensor limitations e.g., in range and incidence. The other issue is the need to ensure sufficient overlap between each new local model and the current map, in order to allow registration of successive views under positioning uncertainties inherent to mobile robots. To address both issues in a coherent framework, in this paper we introduce the concept of a safe region, defined as the largest region that is guaranteed to be free of obstacles given the sensor readings made so far. The construction of a safe region takes sensor limitations into account. In this paper we also describe an NBV algorithm that uses the safe-region concept to select the next robot position at each step. The new position is chosen within the safe region in order to maximize the expected gain of information under the constraint that the local model at this new position must have a minimal overlap with the current global map. In the future, NBV and SLAM algorithms should reinforce each other. While a SLAM algorithm builds a map by making the best use of the available sensory data, an NBV algorithm, such as that proposed here, guides the navigation of the robot through positions selected to provide the best sensory inputs.


title: Preschoolers Mistrust Ignorant and Inaccurate Speakers

abstract: Being able to evaluate the accuracy of an informant is essential to communication. Three experiments explored ::: preschoolers’ (N5119) understanding that, in cases of conflict, information from reliable informants is preferable ::: to information from unreliable informants. In Experiment 1, children were presented with previously ::: accurate and inaccurate informants who presented conflicting names for novel objects. 4-year-olds but not 3- ::: year-olds predicted whether an informant would be accurate in the future, sought, and endorsed information ::: from the accurate over the inaccurate informant. In Experiment 2, both age groups displayed trust in knowledgeable ::: over ignorant speakers. In Experiment 3, children extended selective trust when learning both verbal ::: and nonverbal information. These experiments demonstrate that preschoolers have a key strategy for assessing ::: the reliability of information.


title: Automated Bangla text summarization by sentence scoring and ranking

abstract: In Natural Language Processing (NLP) the document summarization is an area that is getting interest of modern researchers. Though there are many techniques that have been proposed for English language but a few notable works have been done for Bangla text summarization. This paper deals with the development of an extraction based summarization technique which works on Bangla text documents. The system summarizes a single document at a time. Before creating the summary of a document, it is pre-processed by tokenization, removal of stop words and stemming. In the document summarization process, the countable features like word frequency and sentence positional value are used to make the summary more precise and concrete. Attributes like cue words and skeleton of the document are included in the process, which help to make the summary more relevant to the content of the document. The proposed technique has been compared with summary of documents generated by human professionals. The evaluation shows that 83.57% of summary sentences selected by the system agreed with those made by human.


title: Warpage Measurements and Characterizations of Fan-Out Wafer-Level Packaging with Large Chips and Multiple Redistributed Layers

abstract: In this paper, the warpages of a chip-first and die face-up fan-out wafer-level packaging (FOWLP) with a very large silicon chip (10 mm $\times \,\, 10$ mm $\times \,\, 0.15$ mm) and three redistributed layers are measured and characterized. Emphasis is placed on the measurement and 3-D finite-element simulation of the warpages during the FOWLP fabrication processes, especially for: 1) right after postmold cure; 2) right after backgrinding of the epoxy molding compound to expose the Cu-contact pads; and 3) the individual package (right after the solder ball mounting and dicing) versus surface mount technology reflow temperatures. The simulation results are compared to the measurement results. Some recommendations on controlling the warpages are provided.


title: Environmentally Friendly Iron-Catalyzed Cascade Synthesis of 1,2,4-Benzothiadiazine 1,1-Dioxide and Quinazolinone Derivatives

abstract: We have developed an efficient iron-catalyzed method for the cascade synthesis of 1,2,4-benzothiadiazine 1,1-dioxide and quinazolinone derivatives. The protocol uses inexpensive and environmentally friendly FeCl3 as the catalyst, and no ligand or additive was required. This is the first example of construction of nitrogen-containing heterocycles via iron-catalyzed N-arylation in the absence of ligand. Therefore, this method is of practical application for the synthesis of the two different nitrogen-containing heterocycles.


title: Lichenoid vulvar disease: A review☆

abstract: Vulvar dermatoses are common, potentially debilitating conditions that can be seen by a variety of medical specialists. Lichenoid vulvar diseases, namely lichen sclerosus (LS), lichen planus (LP), and lichen simplex chronicus (LSC), can all negatively impact patients' quality of life and LS and LP also have an association with squamous cell carcinoma. It is essential that dermatologists are familiar with the unique features of each of these conditions to ensure the appropriate management and follow up. Herein, we provide an update on the epidemiology, clinical presentation, histopathology, and treatment of patients with vulvar LS, LP, and LSC.


title: A layered design approach for mobile tourism

abstract: The mobile tourism domain has known a new generation of mobile touristic applications that guides tourists in their travels, to provide all necessary information about local tourist attractions of a region, hotels, restaurants, online reservations … however the information is not always relevant to the user and does not meet their needs at any time and any place. For this reason we have used a set of mobile technologies and methods to achieve an approach to m-tourism providing the user touristic recommendations based on its context and preferences to meet their needs.


title: A Combinatorial, Primal-Dual Approach to Semidefinite Programs

abstract: Semidefinite programs (SDP) have been used in many recentapproximation algorithms. We develop a general primal-dualapproach to solve SDPs using a generalization ofthe well-known multiplicative weights update rule to symmetricmatrices. For a number of problems, such as Sparsest Cut and Balanced Separator in undirected and directed weighted graphs, and the Min UnCut problem, this yields combinatorial approximationalgorithms that are significantly more efficient than interiorpoint methods. The design of our primal-dual algorithms is guidedby a robust analysis of rounding algorithms used to obtain integersolutions from fractional ones.


title: A nonparametric statistical method for image segmentation using information theory and curve evolution

abstract: In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training.


title: The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

abstract: In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge gradient method. By construction, this method provides the one-step Bayes-optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.


title: Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones

abstract: Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e., distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e., Weaving , Swerving , Sideslipping , Fast U-turn , Turning with a wide radius , and Sudden braking . Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a fine-grained abnormal D riving behavior D etection and i D entification system, $D^{3}$ , to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. We extract effective features to capture the patterns of abnormal driving behaviors. After that, two machine learning methods, Support Vector Machine (SVM) and Neuron Networks (NN), are employed, respectively, to train the features and output a classifier model which conducts fine-grained abnormal driving behaviors detection and identification. From results of extensive experiments with 20 volunteers driving for another four months in real driving environments, we show that $D^{3}$ achieves an average total accuracy of 95.36 percent with SVM classifier model, and 96.88 percent with NN classifier model.


title: On deep generative models with applications to recognition

abstract: The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to use statistical learning tools to model the dependencies among these features and eventual labels. Learning probabilistic models directly on the raw pixel values has proved to be much more difficult and is typically only used for regularizing discriminative methods. In this work, we use one of the best, pixel-level, generative models of natural images–a gated MRF–as the lowest level of a deep belief network (DBN) that has several hidden layers. We show that the resulting DBN is very good at coping with occlusion when predicting expression categories from face images, and it can produce features that perform comparably to SIFT descriptors for discriminating different types of scene. The generative ability of the model also makes it easy to see what information is captured and what is lost at each level of representation.


title: Deep Multi-camera People Detection

abstract: This paper addresses the problem of multi-view people occupancy map estimation. Existing solutions either operate per-view, or rely on a background subtraction preprocessing. Both approaches lessen the detection performance as scenes become more crowded. The former does not exploit joint information, whereas the latter deals with ambiguous input due to the foreground blobs becoming more and more interconnected as the number of targets increases. Although deep learning algorithms have proven to excel on remarkably numerous computer vision tasks, such a method has not been applied yet to this problem. In large part this is due to the lack of large-scale multi-camera data-set. The core of our method is an architecture which makes use of monocular pedestrian data-set, available at larger scale than the multi-view ones, applies parallel processing to the multiple video streams, and jointly utilises it. Our end-to-end deep learning method outperforms existing methods by large margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly available a new three-camera HD data-set.


title: Detection of Glycoproteins in Polyacrylamide Gels Using Pro-Q Emerald 300 Dye, a Fluorescent Periodate Schiff-Base Stain

abstract: Pro-Q Emerald 300 glycoprotein stain generates a bright-green fluorescent signal upon reacting with periodic acid-oxidized carbohydrate groups on proteins. With this dye it is possible to detect proteins directly in the gel without the need to transfer them to a membrane. This dye is more sensitive than the standard periodic-acid Schiff's base which uses acidic fuchsin dye.


title: A novel target separation algorithm applied to the two-dimensional spectrum for FMCW automotive radar systems

abstract: In this paper, we apply a high-resolution approach, i.e. the matrix pencil method (MPM), to the FMCW automotive radar system to separate the neighboring targets, which share similar parameters, i.e. range, relative speed and azimuth angle, and cause overlapping in the radar spectrum. In order to adapt the 1D model of MPM to the 2D range-velocity spectrum and simultaneously limit the computational cost, some preprocessing steps are proposed to construct a novel separation algorithm. Finally, this algorithm is evaluated in both simulation and real data, and the results indicate a promising performance.


title: Can twitter posts predict stock behavior?: A study of stock market with twitter social emotion

abstract: With the applications of clouds computing and social network, it is of great interests for investors to grasp the variation and predict the trend in stock market. Because of too many factors that affect the stock market, it is quite difficult for a thorough understanding and successful prediction of the variation and the trend of stock market. By retrieving the emotional vocabulary in twitter social media, this paper aims to find the relevance of twitter users' mood change when they share tweets regarding the company's stock price trends. The study also implies some more accurate and efficient ways to improve the accuracy of prediction of the stock.


title: Evaluation of a wireless infrared thermometer with a narrow field of view

abstract: Many agricultural studies rely on infrared sensors for remote measurement of surface temperatures for crop status monitoring and estimating sensible and latent heat fluxes. Historically, applications for these non-contact thermometers employed the use of hand-held or stationary industrial infrared thermometers (IRTs) wired to data loggers. Wireless sensors in agricultural applications are a practical alternative, but the availability of low cost wireless IRTs is limited. In this study, we designed prototype narrow (10^o) field of view wireless infrared sensor modules and evaluated the performance of the IRT sensor by comparing temperature readings of an object (T"o"b"j) against a blackbody calibrator in a controlled temperature room at ambient temperatures of 15^oC, 25^oC, 35^oC, and 45^oC. Additional comparative readings were taken over plant and soil samples alongside a hand-held IRT and over an isothermal target in the outdoors next to a wired IRT. The average root mean square error (RMSE) and mean absolute error (MAE) between the collected IRT object temperature readings and the blackbody target ranged between 0.10 and 0.79^oC. The wireless IRT readings also compared well with the hand-held IRT and wired industrial IRT. Additional tests performed to investigate the influence of direct radiation on IRT measurements indicated that housing the sensor in white polyvinyl chloride provided ample shielding for the self-compensating circuitry of the IR detector. The relatively low cost of the wireless IRT modules and repeatable measurements against a blackbody calibrator and commercial IR thermometers demonstrated that these wireless prototypes have the potential to provide accurate surface radiometric temperature readings in outdoor applications. Further studies are needed to thoroughly test radio frequency communication and power consumption characteristics in an outdoor setting.


A PCG : A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation


title: Measuring the diffusion of an innovation: A citation analysis

abstract: Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the crossâ€ boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.


title: A Subspace Learning Approach to High-Dimensional Matrix Decomposition with Efficient Information Sampling

abstract: This paper is concerned with the problem of low-rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems that scale with the dimension of the data, which limit their scalability. Furthermore, the existing randomized approaches mostly rely on uniform random sampling, which can be quite inefficient for many real world data matrices that exhibit additional structures (e.g. clustering). In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed. The decomposition is carried out using a small data sketch formed from sampled columns/rows. Even when the data is sampled uniformly at random, it is shown that the sufficient number of sampled columns/rows is roughly O(r \mu), where \mu is the coherency parameter and r the rank of the low-rank component. In addition, efficient sampling algorithms are proposed to address the problem of column/row sampling from structured data. The proposed sampling algorithms can be independently used for feature selection from high-dimensional data. The proposed approach is amenable to online implementation and an online scheme is proposed.


title: A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space

abstract: Dimensional models have been proposed in psychology studies to represent complex human emotional expressions. Activation and valence are two common dimensions in such models. They can be used to describe certain emotions. For example, anger is one type of emotion with a low valence and high activation value; neutral has both a medium level valence and activation value. In this work, we propose to apply multi-task learning to leverage activation and valence information for acoustic emotion recognition based on the deep belief network (DBN) framework. We treat the categorical emotion recognition task as the major task. For the secondary task, we leverage activation and valence labels in two different ways, category level based classification and continuous level based regression. The combination of the loss functions from the major and secondary tasks is used as the objective function in the multi-task learning framework. After iterative optimization, the values from the last hidden layer in the DBN are used as new features and fed into a support vector machine classifier for emotion recognition. Our experimental results on the Interactive Emotional Dyadic Motion Capture and Sustained Emotionally Colored Machine-Human Interaction Using Nonverbal Expression databases show significant improvements on unweighted accuracy, illustrating the benefit of utilizing additional information in a multi-task learning setup for emotion recognition.


title: Heat kernel based community detection

abstract: The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank diffusion, is useful in identifying a community nearby a starting seed node. We present the first deterministic, local algorithm to compute this diffusion and use that algorithm to study the communities that it produces. Our algorithm is formally a relaxation method for solving a linear system to estimate the matrix exponential in a degree-weighted norm. We prove that this algorithm stays localized in a large graph and has a worst-case constant runtime that depends only on the parameters of the diffusion, not the size of the graph. On large graphs, our experiments indicate that the communities produced by this method have better conductance than those produced by PageRank, although they take slightly longer to compute. On a real-world community identification task, the heat kernel communities perform better than those from the PageRank diffusion.


title: Exceptionization: A Java VM Optimization for Non-Java Languages

abstract: Java virtual machine (JVM) has recently evolved into a general-purpose language runtime environment to execute popular programming languages such as JavaScript, Ruby, Python, and Scala. These languages have complex non-Java features, including dynamic typing and first-class function, so additional language runtimes (engines) are provided on top of the JVM to support them with bytecode extensions. Although there are high-performance JVMs with powerful just-in-time (JIT) compilers, running these languages efficiently on the JVM is still a challenge. This article introduces a simple and novel technique for the JVM JIT compiler called exceptionization to improve the performance of JVM-based language runtimes. We observed that the JVM executing some non-Java languages encounters at least 2 times more branch bytecodes than Java, most of which are highly biased to take only one target. Exceptionization treats such a highly biased branch as some implicit exception-throwing instruction. This allows the JVM JIT compiler to prune the infrequent target of the branch from the frequent control flow, thus compiling the frequent control flow more aggressively with better optimization. If a pruned path were taken, then it would run like a Java exception handler, that is, a catch block. We also devised de-exceptionization, a mechanism to cope with the case when a pruned path is executed more often than expected. Since exceptionization is a generic JVM optimization, independent of any specific language runtime, it would be generally applicable to other language runtimes on the JVM. Our experimental result shows that exceptionization accelerates the performance of several non-Java languages. For example, JavaScript-on-JVM runs faster by as much as 60% and by 6% on average, when experimented with the Octane benchmark suite on Oracle’s latest Nashorn JavaScript engine and HotSpot 1.9 JVM. Furthermore, the performance of Ruby-on-JVM shows an improvement by as much as 60% and by 6% on average, while Python-on-JVM improves by as much as 6% and by 2% on average. We found that exceptionization is more effective to apply to the branch bytecode of the language runtime itself than the bytecode corresponding to the application code or the bytecode of the Java class libraries. This implies that the performance benefit of exceptionization comes from better JIT compilation of the language runtime of non-Java languages.


title: A salt-induced butA gene of Tetragenococcus halophilus confers salt tolerance to Escherichia coli by heterologous expression of its dual copies

abstract: Accumulating glycine betaine through the ButA transport system from an exogenous supply is a survival strategy employed by Tetragenococcus halophilus, a moderate halophilic lactic acid bacterium with crucial role in flavor formation of high-salt food fermentation, to achieve cellular protection. In this study, we firstly confirmed that butA expression was up-regulated under salt stress conditions by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Subsequently, we discovered that recombinant Escherichia coli MKH13 strains with single- and double-copy butA complete expression box(es) showed typical growth curves while they differed in their salt adaption and tolerance. Meanwhile, high-performance liquid chromatography (HPLC) experiments confirmed results obtained from growth curves. In summary, our results indicated that regulation of butA expression was salt-induced and double-copy butA cassettes entrusted a higher ability of salt adaption and tolerance to E. coli MKH13, which implied the potential of muti-copies of butA gene in the genetic modification of T. halophilus for improvement of salt tolerance and better industrial application.


title: Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.

abstract: OBJECTIVE ::: Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. We hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. ::: ::: ::: METHOD ::: A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980). ::: ::: ::: RESULTS ::: Mixed-effects linear modeling showed that sensor-derived geospatial activity (p < .05), sleep duration (p < .05), and variability in geospatial activity (p < .05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p < .05), geospatial activity (p < .05), and sleep duration (p < .05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p < .01). ::: ::: ::: CONCLUSIONS AND IMPLICATIONS FOR PRACTICE ::: Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies.


title: Advances in coal spectroscopy

abstract: Application of SEM Techniques to the Characterization of Coal and Coal Ash Products M.L. Jones, et al. XAFS Spectroscopy Studies of Critical Elements in Coal and Coal Derivatives G.P. Huffman, et al. Investigation of Coal Surfaces by ESCA (XPS) T. Szekely, et al. Tracking Forms of Sulfur in Coal Research Using Natural 34S/32S Ratios K.C. Hackley, et al. Laser Spark Emission Spectroscopy of Individual Coal Particles D.K. Ottesen. InSitu Microspectrophotometry of Coal Macerals D.F. Bensley, et al. Structural Comparison of LowMolecularWeight Extractable Compounds in Different Rank Coals Using Capillary Column Gas Chromatography H.C.K. Chang, et al. Combined Chromatography and FTIR Spectrometry of Coal Extracts P.R. Griffiths, et al. Proton NMR Spectroscopy of Coals, Pitches and Coal Derived Liquids B.C. Gerstein, et al. 13C NMR Techniques for Structural Studies of Coals and Coal Chars A.M. Orendt, et al. Mass Spectrometric Studies of Coals and Coal Macerals R.E. Winans. Computer-Enhanced Pyrolysis Mass Spectrometry: A New Window on Coal Structure and Reactivity H.L.C. Meuzelaar, et al. Thermochemical Analysis of U.S. Argonne Premium Coal Samples by Time-Resolved Pyrolysis Field Ionization Mass Spectrometry N. Simmleit, et al. OnLine Fourier Transform Infrared Spetroscopy in Coal Research P.R. Solomon. 2 additional articles. Index.


title: Detection of Ellipses by a Modified Hough Transformation

abstract: The Hough transformation can detect straight lines in an edge-enhanced picture, however its extension to recover ellipses requires too long a computing time. This correspondence proposes a modified method which utilizes two properties of an ellipse in such a way that it iteratively searches for clusters in two different parameter spaces to find almost complete ellipses, then evaluates their parameters by the least mean squares method.


title: Metrics for human driving of multiple robots

abstract: A goal of human-robot interaction is to expand the capability of human beings so that they can control multiple robots simultaneously. This provides leverage for human attention beyond single robot interaction. The number of such robots that can be operated is called the fan-out of a human-robot team. We define the concepts of neglect-tolerance as a measure of autonomy and interaction-effort as a measure of required human attention. A model for fan-out is presented that predicts a human's ability to control multiple robots. We validate this model using robot simulations and user studies. Using the validated model we demonstrate enhanced task performance on physical robots as predicted by the model.


title: The chatbot feels you - a counseling service using emotional response generation

abstract: Early study tries to use chatbot for counseling services. They changed drinking habit of who being consulted by leading them via intervene chatbot. However, the application did not concerned about psychiatric status through continuous conversation with user monitoring. Furthermore, they had no ethical judgment method that about the intervention of the chatbot. We argue that more reasonable and continuous emotion recognition will make better mental healthcare experiment. It will be more proper clinical psychiatric consolation in ethical view as well. This paper suggests a introduce a novel chatbot system for psychiatric counseling service. Our system understands content of conversation based on recent natural language processing (NLP) methods with emotion recognition. It senses emotional flow through the continuous observation of conversation. Also, we generate personalized counseling response from user input, to do this, we use additional constrains to generation model for the proper response generation which can detect conversational context, user emotion and expected reaction.


title: QUANTUM KINETIC ELECTRON-PHONON INTERACTION IN GAAS: ENERGY NONCONSERVING SCATTERING EVENTS AND MEMORY EFFECTS

abstract: Highly energetic electrons in GaAs emitting LO phonons are studied via femtosecond transmission spectroscopy. It is demonstrated for the first time that energy is not conserved on a time scale as short as a LO oscillation period: A replica of the initial distribution starts spectrally broadened before it sharpens into the excitation shape. This scenario is repeated for every step in the phonon cascade. Simulations combining an exact solution of quantum kinetics within a simplified model and a calculation of the polarization including Coulomb effects quantitatively agree with the data.


title: Digiart: Building new 3D cultural heritage worlds

abstract: The use of serious game (SG) for learning is now further extended in cultural heritage (CH). SGs simulate the real world through the application of 3D models that initiates interaction, imagination and trigger the immersion of the user. The design and development of three SGs that gamify three case studies of european CH are presented, in the context of Digiart European Union funded project. The first case study concerns the Scladina cave in Belgium. The second SG simulates an anthropology virtual museum for Liverpool John Moores University in United Kingdom. The third case study communicates with the public, the Palace of Aigai in Greece. Pioneering techniques have been used for generating accurate 3D models such as archaeological sites and artifacts. The embodiment of these assets into an interactive and fully immersive virtual environment of a game engine is the subject of this report.


title: Algorithmic Composition of Melodies with Deep Recurrent Neural Networks

abstract: A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated mu- sic composers able to generate new melodies coherent with the style they have been trained on. We employ gated-recurrent unit (GRU) networks that have been shown to be particularly efficient in learning complex sequential activations with arbitrary long time lags. Our model processes rhythm and melody in parallel while modeling the relation between these two properties. Using such an approach, we were able to generate interesting complete melodies or suggest possible continuations of a melody fragment that is coherent with the characteristics of the fragment itself.


title: The pothole patrol: using a mobile sensor network for road surface monitoring

abstract: This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.


Sustainable funding for biocuration: The Arabidopsis Information Resource (TAIR) as a case study of a subscription-based funding model


title: Exomuscle: An inflatable device for shoulder abduction support

abstract: Stroke is the leading cause of adult disability. Many robots have been developed to administer movement therapies or provide physical assistance to stroke survivors suffering from movement deficits. One effective approach has been to support the weight of the arm, offloading shoulder abductor muscles that have become coupled to elbow muscles. However, patients have limited access to such robots due to the robots' complexity, cost, and bulk. To counter this problem, we developed a lightweight (350 g), inexpensive external actuator, which we call an exomuscle. We constructed a prototype exomuscle by reinforcing a plastic bladder with a fabric bag that is sewn to supporting straps. The bladder can then be inflated with pressurized air to provide expansive forces between the user's torso and arm, supporting shoulder abduction. A seam acting as a hinge joint connects the exomuscle to the torso. We demonstrate that our exomuscle reduces muscular effort by 74% in isometric tasks and 72% in dynamic reaching tasks while minimally affecting the range of motion of the shoulder and elbow (average 4% reduction) on three users ranging from 165 to 188 cm tall. Future studies will evaluate the exomuscle with users who have post-stroke motor impairments.


title: Accelerometer-based fetal movement detection

abstract: Monitoring fetal wellbeing is a compelling problem in modern obstetrics. Clinicians have become increasingly aware of the link between fetal activity (movement), well-being, and later developmental outcome. We have recently developed an ambulatory accelerometer-based fetal activity monitor (AFAM) to record 24-hour fetal movement. Using this system, we aim at developing signal processing methods to automatically detect and quantitatively characterize fetal movements. The first step in this direction is to test the performance of the accelerometer in detecting fetal movement against real-time ultrasound imaging (taken as the gold standard). This paper reports first results of this performance analysis.


title: Radiation from a PCB trace with broadband harmonic frequency signals

abstract: In this paper, radiation from a PCB trace with broadband harmonic frequency signals is proposed. For the digital signal, there are high harmonic frequency component current. These current will cause the radiated emissions which is called electromagnetic interference. Some of the radiated emissions emit or couple from the PCB traces. It means that the PCB traces act as the antenna. We design some experiments to study and analyze the radiated emissions mechanism from 30MHz to 1000 MHz. The measurement was performed in 3-m semi-anechoic chamber. It's usually used for EMI test. We found the magnitude of emissions increase with the longer length of PCB traces below 500 MHz. But above the 500 MHz, the resonant frequency length and transmission line dominate the radiation. For this paper researching results can help the PCB design and layout engineers to choose the best trace length to reduce the EMI emissions effectively.


title: Does stress damage the brain?

abstract: Studies in animals showed that stress results in damage to the hippocampus, a brain area involved in learning and memory, with associated memory deficits. The mechanism involves glucocorticoids and possibly serotonin acting through excitatory amino acids to mediate hippocampal atrophy. Patients with posttraumatic stress disorder (PTSD) from Vietnam combat and childhood abuse had deficits on neuropsychological measures that have been validated as probes of hippocampal function. In addition, magnetic resonance imaging (MRI) showed reduction in volume of the hippocampus in both combat veterans and victims of childhood abuse. In combat veterans, hippocampal volume reduction was correlated with deficits in verbal memory on neuropsychological testing. These studies introduce the possibility that experiences in the form of traumatic stressors can have long-term effects on the structure and function of the brain.


title: 100 top-cited scientific papers in limb prosthetics

abstract: Research has tremendously contributed to the developments in both practical and fundamental aspects of limb prosthetics. These advancements are reflected in scientific articles, particularly in the most cited papers. This article aimed to identify the 100 top-cited articles in the field of limb prosthetics and to investigate their main characteristics. Articles related to the field of limb prosthetics and published in the Web of Knowledge database of the Institute for Scientific Information (ISI) from the period of 1980 to 2012. The 100 most cited articles in limb prosthetics were selected based on the citation index report. All types of articles except for proceedings and letters were included in the study. The study design and level of evidence were determined using Sackett’s initial rules of evidence. The level of evidence was categorized either as a systematic review or meta-analysis, randomized controlled trial, cohort study, case–control study, case series, expert opinion, or design and development. The top cited articles in prosthetics were published from 1980 to 2012 with a citation range of 11 to 90 times since publication. The mean citation rate was 24.43 (SD 16.7) times. Eighty-four percent of the articles were original publications and were most commonly prospective (76%) and case series studies (67%) that used human subjects (96%) providing level 4 evidence. Among the various fields, rehabilitation (47%), orthopedics (29%), and sport sciences (28%) were the most common fields of study. The study established that studies conducted in North America and were written in English had the highest citations. Top cited articles primarily dealt with lower limb prosthetics, specifically, on transtibial and transradial prosthetic limbs. Majority of the articles were experimental studies.


title: SLA-Based Trust Model for Cloud Computing

abstract: Cloud computing is a new form of technology, which infrastructure, developing platform, software, and storage can be delivered as a service in a pay as you use cost model. However, for critical business application and more sensitive information, cloud providers must be selected based on high level of trustworthiness. In this paper, we present a trust model to evaluate cloud services in order to help cloud users select the most reliable resources. We integrate our previous work “conceptual SLA framework for cloud computing” with the proposed trust management model to present a new solution of defining the reliable criteria for the selection process of cloud providers


title: INTERPERSONAL RELATIONS AND GROUP PROCESSES Emotional Selection in Memes: The Case of Urban Legends

abstract: This article explores how much memes like urban legends succeed on the basis of informational selection (i.e., truth or a moral lesson) and emotional selection (i.e., the ability to evoke emotions like anger, fear, or disgust). The article focuses on disgust because its elicitors have been precisely described. In Study 1, with controls for informational factors like truth, people were more willing to pass along stories that elicited stronger disgust. Study 2 randomly sampled legends and created versions that varied in disgust; people preferred to pass along versions that produced the highest level of disgust. Study 3 coded legends for specific story motifs that produce disgust (e.g., ingestion of a contaminated substance) and found that legends that contained more disgust motifs were distributed more widely on urban legend Web sites. The conclusion discusses implications of emotional selection for the social marketplace of ideas.


title: Calculation of efficiency of the project of modernization machining shop of the instrument-making enterprise

abstract: One of the directions of investment activity of the enterprise is investment into fixed assets which include processing equipment. Investment into processing equipment demands presence at the subject of information directorate about expenses and results of operation of the available equipment, knowledge of a current state of development of technology, ability to allocate various versions of projects of modernization, to estimate costs of acquisition, development and operation of the new equipment, abilities to compare projects in the specified parameters, to estimate their expediency and economic efficiency In work modern problems of achievement by the Russian industry of the level of efficiency and competitiveness meeting the requirements of the world market of the knowledge-intensive and hi-tech production, realization of innovative development of the enterprises with ensuring the corresponding growth rates for indicators of economic efficiency are considered. In this article the example of calculation of efficiency of the project of modernization of machining shop of the instrument-making enterprise (St. Petersburg) for participation in the FTP is given. The used numerical values have exclusively demonstration character.


title: Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks

abstract: Graphical abstractDisplay Omitted HighlightsWe predict maximum and minimum day stock prices of power companies.The methodology is based on attribute selection and time series prediction.The most relevant attributes are determined by correlation analysis.The actual time series prediction is carried out by neural networks.The proposed methodology provides very good results. Time series forecasting has been widely used to determine future prices of stocks, and the analysis and modeling of finance time series is an important task for guiding investors' decisions and trades. Nonetheless, the prediction of prices by means of a time series is not trivial and it requires a thorough analysis of indexes, variables and other data. In addition, in a dynamic environment such as the stock market, the non-linearity of the time series is a pronounced characteristic, and this immediately affects the efficacy of stock price forecasts. Thus, this paper aims at proposing a methodology that forecasts the maximum and minimum day stock prices of three Brazilian power distribution companies, which are traded in the Sao Paulo Stock Exchange BM&FBovespa. When compared to the other papers already published in the literature, one of the main contributions and novelty of this paper is the forecast of the range of closing prices of Brazilian power distribution companies' stocks. As a result of its application, investors may be able to define threshold values for their stock trades. Moreover, such a methodology may be of great interest to home brokers who do not possess ample knowledge to invest in such companies. The proposed methodology is based on the calculation of distinct features to be analysed by means of attribute selection, defining the most relevant attributes to predict the maximum and minimum day stock prices of each company. Then, the actual prediction was carried out by Artificial Neural Networks (ANNs), which had their performances evaluated by means of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) calculations. The proposed methodology for addressing the problem of prediction of maximum and minimum day stock prices for Brazilian distribution companies is effective. In addition, these results were only possible to be achieved due to the combined use of attribute selection by correlation analysis and ANNs.


title: The limits of software transactional memory (STM): dissecting Haskell STM applications on a many-core environment

abstract: In this paper, we present a Haskell Transactional Memory benchmark to provide a comprehensive application suite for the use of Software Transactional Memory (STM) researchers. We develop a framework to profile the execution of the benchmark applications and to collect detailed runtime data on their transactional behavior, running them on a 128-core multiprocessor. Using a composite of the collected raw data, we propose new transactional performance metrics. We analyze key statistics related to scalability, atomic sections, transactional events, overall transactional overhead and the relative hardware performance, accordingly drawing conclusions on the results. Our findings advance our comprehension on the STM runtime and the characteristics of different applications under the transactional management of the pure, functional programming language, Haskell.


title: High-Input-Voltage High-Frequency Class E Rectifiers for Resonant Inductive Links

abstract: The operation of traditional rectifiers such as half-wave and bridge rectifiers in wireless power transfer applications may be inefficient and can reduce the amount of power that is delivered to a load. An alternative is to use Class E resonant rectifiers that are known to operate efficiently at high resonant frequencies and at large input voltages. Class E rectifiers have a near sinusoidal input current which leads to an improved overall system performance and increased efficiency, especially that of the transmitting coil driver. This paper is the first to investigate the use of Class E resonant rectifiers in wireless power transfer systems based on resonant inductive coupling. A piecewise linear state-space representation is used to model the Class E rectifier including the rectifying diode's forward voltage drop, its ON resistance, and the equivalent series resistance of the resonant inductor. Power quality parameters, such as power factor and total harmonic distortion, are calculated for different loading conditions. Extensive experimental results based on a 10-W prototype are presented to confirm the performed analysis and the efficient operation of the rectifier. An impressive operating efficiency of 94.43% has been achieved at a resonant frequency of 800 kHz.


title: Alive-FP: Automated Verification of Floating Point Based Peephole Optimizations in LLVM

abstract: Peephole optimizations optimize and canonicalize code to enable other optimizations but are error-prone. Our prior research on Alive, a domain-specific language for specifying LLVM’s peephole optimizations, automatically verifies the correctness of integer-based peephole optimizations and generates C++ code for use within LLVM. This paper proposes Alive-FP, an automated verification framework for floating point based peephole optimizations in LLVM. Alive-FP handles a class of floating point optimizations and fast-math optimizations involving signed zeros, not-a-number, and infinities, which do not result in loss of accuracy. This paper provides multiple encodings for various floating point operations to account for the various kinds of undefined behavior and under-specification in the LLVM’s language reference manual. We have translated all optimizations that belong to this category into Alive-FP. In this process, we have discovered seven wrong optimizations in LLVM.


abstract: This paper presents a new disk I/O architecture composed of an array of a flash memory SSD (solid state disk) and a hard disk drive (HDD) that are intelligently coupled by a special algorithm. We call this architecture I-CASH: Intelligently Coupled Array of SSD and HDD. The SSD stores seldom-changed and mostly read reference data blocks whereas the HDD stores a log of deltas between currently accessed I/O blocks and their corresponding reference blocks in the SSD so that random writes are not performed in SSD during online I/O operations. High speed delta compression and similarity detection algorithms are developed to control the pair of SSD and HDD. The idea is to exploit the fast read performance of SSDs and the high speed computation of modern multi-core CPUs to replace and substitute, to a great extent, the mechanical operations of HDDs. At the same time, we avoid runtime SSD writes that are slow and wearing. An experimental prototype I-CASH has been implemented and is used to evaluate I-CASH performance as compared to existing SSD/HDD I/O architectures. Numerical results on standard benchmarks show that I-CASH reduces the average I/O response time by an order of magnitude compared to existing disk I/O architectures such as RAID and SSD/HDD storage hierarchy, and provides up to 2.8 speedup over state-of-the-art pure SSD storage. Furthermore, I-CASH reduces random writes to SSD implying reduced wearing and prolonged life time of the SSD.


title: Yield-aware Performance-Cost Characterization for Multi-Core SIMT

abstract: Redundancy is now routinely allocated in circuits, microarchitectural structures, or at the system level, to mitigate mounting manufacturing yield losses. In this paper, we propose spare lane sharing, which reduces the cost of multi-core SIMT systems by allowing one of two neighboring cores to make use of a redundant lane if necessary. We have evaluated the performance-cost trade-offs of core-, lane-, and shared-lane-sparing under a variety of benchmarks, and found that for nearly all applications shared-lane-sparing outperforms lane-sparing, reducing cost by up to 20%.


title: SQL Query Optimization Methods of Relational Database System

abstract: Oracle provides several options to aid performance, such as partitioning large tables, using materialized views, storing plan outlines, and many others. This chapter examines how DBAs can use these techniques to aid developers’ efforts to increase the efficiency of their application code. This chapter introduces the SQL Tuning Advisor to help you tune SQL statements. You can then use the recommendations of this advisor to rewrite poorly performing SQL code. I begin the chapter with a discussion of how to approach performance tuning. More than the specific performance improvement techniques you use, your approach to performance tuning determines your success in tuning a recalcitrant application system.


title: Unifying the Smart Home Experience Through HbbTV-enabled Devices

abstract: In the current context of seamless integration for a variety of devices within the Smart Home concept, the home TV screen has gained again a pivotal role in everyday life, acting as a central hub for audio and video broadcasting services, home automation and other devices, including smartphones, tablets and IoT - enabled devices. In this context, a standard compliant and platform independent solution for the television device is preferred as it can ensure a dynamic environment in which various devices are able to contribute to the overall user experience. This paper proposes an architecture which seamlessly integrates the consumer’s phone and a smart home environment with a HbbTV-enabled television set, using the HbbTV 2.0 Companions Screen and Multimedia Synchronization framework. A first architectural design and its hardware implementation have been developed and deployed as a proof of concept. An initial set of tests has been conducted in order to assess the benefits of the proposed system.


9 PID Control Theory


Is there Really a Need for Using NLP to Elicit Requirements? A Benchmarking Study to Assess Scalability of Manual Analysis


title: Comparison of postsimulation debriefing versus in-simulation debriefing in medical simulation.

abstract: Introduction:A key portion of medical simulation is self-reflection and instruction during a debriefing session; however, there have been surprisingly few direct comparisons of various approaches. The objective of this study was to compare two styles of managing a simulation session: postsimulation


title: Long-term Mapping Techniques for Ship Hull Inspection and Surveillance using an Autonomous Underwater Vehicle

abstract: This paper reports on a system for an autonomous underwater vehicle to perform in situ, multiple session hull inspection using long-term simultaneous localization and mapping SLAM. Our method assumes very little a priori knowledge, and it does not require the aid of acoustic beacons for navigation, which is a typical mode of navigation in this type of application. Our system combines recent techniques in underwater saliency-informed visual SLAM and a method for representing the ship hull surface as a collection of many locally planar surface features. This methodology produces accurate maps that can be constructed in real-time on consumer-grade computing hardware. A single-session SLAM result is initially used as a prior map for later sessions, where the robot automatically merges the multiple surveys into a common hull-relative reference frame. To perform the relocalization step, we use a particle filter that leverages the locally planar representation of the ship hull surface, and a fast visual descriptor matching algorithm. Finally, we apply the recently developed graph sparsification tool, generic linear constraints, as a way to manage the computational complexity of the SLAM system as the robot accumulates information across multiple sessions. We show results for 20 SLAM sessions for two large vessels over the course of days, months, and even up to three years, with a total path length of approximately 10.2 km.


title: Volumetric Image Registration From Invariant Keypoints

abstract: We present a method for image registration based on 3D scale- and rotation-invariant keypoints. The method extends the scale invariant feature transform (SIFT) to arbitrary dimensions by making key modifications to orientation assignment and gradient histograms. Rotation invariance is proven mathematically. Additional modifications are made to extrema detection and keypoint matching based on the demands of image registration. Our experiments suggest that the choice of neighborhood in discrete extrema detection has a strong impact on image registration accuracy. In head MR images, the brain is registered to a labeled atlas with an average Dice coefficient of 92%, outperforming registration from mutual information as well as an existing 3D SIFT implementation. In abdominal CT images, the spine is registered with an average error of 4.82 mm. Furthermore, keypoints are matched with high precision in simulated head MR images exhibiting lesions from multiple sclerosis. These results were achieved using only affine transforms, and with no change in parameters across a wide variety of medical images. This paper is freely available as a cross-platform software library.


ERIK ERIKSON ' S THEORY OF IDENTITY DEVELOPMENT


title: Measurement of Opening and Closing Angles of Aortic Valve Prostheses In Vivo Using Dual-Source Computed Tomography: Comparison with Those of Manufacturers' in 10 Different Types

abstract: Objective: The aims of this study were to compare opening and closing angles of normally functioning mechanical aortic valves measured on dual-source computed tomography (CT) with the manufacturers’ values and to compare CT-measured opening angles according to valve function. Materials and Methods: A total of 140 patients with 10 different types of mechanical aortic valves, who underwent dualsource cardiac CT, were included. Opening and closing angles were measured on CT images. Agreement between angles in normally functioning valves and the manufacturer values was assessed using the interclass coefficient and the BlandAltman method. CT-measured opening angles were compared between normal functioning valves and suspected dysfunctioning valves. Results: The CT-measured opening angles of normally functioning valves and manufacturers’ values showed excellent agreement for seven valve types (intraclass coefficient [ICC], 0.977; 95% confidence interval [CI], 0.962–0.987). The mean differences in opening angles between the CT measurements and the manufacturers’ values were 1.2° in seven types of valves, 11.0° in On-X valves, and 15.5° in ATS valves. The manufacturers’ closing angles and those measured by CT showed excellent agreement for all valve types (ICC, 0.953; 95% CI, 0.920–0.972). Among valves with suspected dysfunction, those with limitation of motion (LOM) and an increased pressure gradient (PG) had smaller opening angles than those with LOM only (p < 0.05). Conclusion: Dual-source cardiac CT accurately measures opening and closing angles in most types of mechanical aortic valves, compared with the manufacturers’ values. Opening angles on CT differ according to the type of valve dysfunction and a decreased opening angle may suggest an elevated PG. Index terms: Mechanical aortic valve; Opening and closing angles; Measurement; Dual-source computed tomography


Comparison of 2.5D and 3D Quantification of Femoral Head Coverage in Normal Control Subjects and Patients with Hip Dysplasia


title: A risk management framework for distributed agile projects

abstract: Abstract Context Distributed agile development (DAD) approach has been adopted by the software companies for cost and time benefits. However, it causes significant challenges considering the contradicting nature of the agile and distributed development. Objective The objective of this study is to develop a risk management framework that comprises the perceived risks in DAD projects, their causes and the methods used in industry for managing those risks. Method This work is an extension of an exploratory study, wherein, DAD practitioners reported the risks they face in projects and the methods they use for managing those risks. The identified risks were further categorized based on their relevance to different aspects of DAD projects. In this extension, industry practitioners ranked the risks for their impact on DAD projects and rated the methods for the frequency of their use in projects. As the number of risks under each category was large for ranking, they were grouped under the risk areas within each category. The ranking of risk categories, risk areas and risk factors for their impact on DAD projects manifests their importance. The framework includes ranked risks, their causes and the risk management approaches. It was partially implemented in live projects in three different companies and was found to be beneficial. Results The perceived impact of the risk categories, ‘Group Awareness’, ‘External Stakeholder Collaboration’ and ‘Software Development Life Cycle’ on DAD projects has been found to be high and caused by the properties of Distributed Software Development (DSD). The partial validation of the framework in three companies reported the elimination of majority of risk factors and/or reduction in their impact. Conclusion DAD projects provide significant benefits but hold substantial risks due to the contradiction between distributed development and agile practices. The reported framework could effectively minimize the DAD risks in practice.


title: Pattern Spectrum and Mu ltiscale Shape Representation

abstract: The results of a study on multiscale shape description, smoothing and representation are reported. Multiscale nonlinear smoothing filters are first developed, using morphological opening and closings. G. Matheron (1975) used openings and closings to obtain probabilistic size distributions of Euclidean-space sets (continuous binary images). These distributions are used to develop a concept of pattern spectrum (a shape-size descriptor). A pattern spectrum is introduced for continuous graytone images and arbitrary multilevel signals, as well as for discrete images, by developing a discrete-size family of patterns. Large jumps in the pattern spectrum at a certain scale indicate the existence of major (protruding or intruding) substructures of the signal at the scale. An entropy-like shape-size complexity measure is also developed based on the pattern spectrum. For shape representation, a reduced morphological skeleton transform is introduced for discrete binary and graytone images. This transform is a sequence of skeleton components (sparse images) which represent the original shape at various scales. It is shown that the partially reconstructed images from the inverse transform on subsequences of skeleton components are the openings of the image at a scale determined by the number of eliminated components; in addition, two-way correspondences are established among the degree of shape smoothing via multiscale openings or closings, the pattern spectrum zero values, and the elimination or nonexistence of skeleton components at certain scales. >


title: Smart Car [Application Notes]

abstract: In contrast to a traditional mechanical car, the Smart Car is a highly computerized automobile featuring ubiquitous computing, intuitive human-computer interaction and an open application platform. In this paper, we propose an advanced Smart Car demonstration platform with a transparent windshield display and various motion sensors where drivers can manipulate a variety of car-appropriate applications in augmented reality. Similar to smartphones, drivers can customize their Smart Car through free downloads of car-appropriate applications according to their needs. Additionally, three potential car-appropriate applications related to computer vision are investigated and implemented in our platform for increased driving safety. The first and second carappropriate applications aim to enhance the driving visual field by restoring the low-visibility scenes captured during inclement-weather or nighttime driving conditions to be high-visibility ones, respectively, and display them on a transparent windshield display. We also survey pedestrian tracking techniques that combine multiple driving recorders' information as a mobile surveillance network, including one proposed framework we have developed as the third car-appropriate application. By embedding these carappropriate applications, the Smart Car has the potential to increase safety of driving conditions both in daytime and nighttime, even in bad weather.


title: Neural Map: Structured Memory for Deep Reinforcement Learning

abstract: A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution over the past k frames or an LSTM layer. More recent work (Oh et al., 2016) has went beyond these architectures by using memory networks which can allow more sophisticated addressing schemes over the past k frames. But even these architectures are unsatisfactory due to the reason that they are limited to only remembering information from the last k frames. In this paper, we develop a memory system with an adaptable write operator that is customized to the sorts of 3D environments that DRL agents typically interact with. This architecture, called the Neural Map, uses a spatially structured 2D memory image to learn to store arbitrary information about the environment over long time lags. We demonstrate empirically that the Neural Map surpasses previous DRL memories on a set of challenging 2D and 3D maze environments and show that it is capable of generalizing to environments that were not seen during training.


title: Dynamic metric learning from pairwise comparisons

abstract: Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures. Here, we address the problem of learning these transformations when the underlying constraint generation process is nonstationary. This nonstationarity can be due to changes in either the ground-truth clustering used to generate constraints or changes in the feature subspaces in which the class structure is apparent. We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD), a general adaptive, online approach for learning and tracking optimal metrics as they change over time that is highly robust to a variety of nonstationary behaviors in the changing metric. We apply the OCELAD framework to an ensemble of online learners. Specifically, we create a retroinitialized composite objective mirror descent (COMID) ensemble (RICE) consisting of a set of parallel COMID learners with different learning rates, demonstrate RICE-OCELAD on both real and synthetic data sets and show significant performance improvements relative to previously proposed batch and online distance metric learning algorithms.


title: A Case of Extensive Wound Myiasis Caused by Lucilia sericata (Diptera: Calliphoridae) in a Patient with Maxillary Sinus Squamous Cell Carcinoma, in Turkey

abstract: The larvae causing myiasis can lead extensive tissue destruction, invasion into deep tissues and secondary infec tions. Poor hygiene, low socioeconomic condition and presence of open wounds are the most important predisposing factors. This case report describes destructive wound myiasis in a 58-year-old male patient diagnosed with maxillary sinus squamous cell carcinoma who lives in a rural area in Ankara, Turkey. Approximately 200 larvae were collected and identified as Lucilia sericata by morphological examination. Myiasis should be considered especially when the patient has open extensive lesions such as malignant wounds.


title: Time Evolution of Properties of SCC Mixtures Produced Using Crushed Limestone Aggregate and High Content of Limestone Filler

abstract: The paper deals with time-dependent increase of compressive strength, splitting tensile strength and modulus of elasticity as well as with compressive stress-strain curves of self-compacting concrete (SCC) made with crushed limestone aggregate and high content of limestone filler. The characteristics under consideration were tested at the ages of 1, 3, 7, 28, 180, 360 and 720 days. Time evolution of the strength characteristics and modulus of elasticity was estimated with analytical models given in European code Eurocode 2. The study revealed that the models can adequately predict time evolution of the SCC characteristics, when coefficient s in the models is equal to 0.17, 0.08 and 0.25 for compressive strength, tensile strength and modulus of elasticity, respectively. These values of coefficient s are quite different from the value 0.2 proposed by Eurocode 2 for used cement strength class.


title: Loop gain analysis and development of high-speed high-accuracy current sensors for switching converters

abstract: Loop gain analysis for performance evaluation of current sensors for switching converters is presented. The MOS transistor scaling technique is reviewed and employed in developing high-speed and high-accuracy current-sensors with offset-current cancellation. Using a standard 0.35/spl mu/m CMOS process, and integrated full-range inductor current sensor for a boost converter is designed. It operated at a supply voltage of 1.5 V with a DC loop gain of 38 dB, and a unity gain frequency of 10 MHz. The sensor worked properly at a converter switching frequency of 500 kHz.


title: Software Engineering Economics

abstract: This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.