title: Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges

abstract: Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.


title: Trade space evaluation of ascent and return architectures for a Mars Sample Return mission

abstract: The concept of Mars Sample Return (MSR) has been considered since the 1960s and is still a top priority for the planetary science community. [1] Although a plan on the number and types of samples to be collected for MSR has been outlined, as articulated in the Mars 2020 Science Definition Team report [2], the trade space of options to return this sample from the surface of Mars to the surface of the Earth is still being explored. One of the main challenges with MSR is that it is inherently a multi-vehicle system where each vehicle's design impacts that of the others. Defining the trade space must therefore be treated as a System of Systems (SoS) problem. The work presented puts forward a framework to rapidly explore such spatially and temporally distributed systems. It investigates the possible vehicle and technology options for MSR, assuming that a packaged sample has been left on the surface of Mars. It also evaluates how launch sequencing choices affect the expected return on investment of different architectures. The paper explores eight key trades, including different types of landing and propulsion systems, as well as low-cost direct return options. A large set of architectures are compared to the baseline proposed in the Planetary Science Decadal Survey [1] for MSR, which consists of a stationary lander, a small fetch rover, a Mars Ascent Vehicle (MAV), and a return orbiter with chemical propulsion. Overall, the baseline is found to be well optimized, although a few options, including the use of solar electric propulsion and of a roving vehicle carrying the MAV to the sample, are shown to offer a better return on investment. Furthermore, when considering only the goals of MSR, an approach where the lander is sent to Mars at least one launch window ahead of the return orbiter is demonstrated to be preferable.


title: The effect of meloxicam on behavior and pain sensitivity of dairy calves following cautery dehorning with a local anesthetic.

abstract: Effects of a single injection of meloxicam on calf behavior, pain sensitivity, and feed and water intakes were examined following dehorning. Sixty Holstein heifer calves were blocked by age and randomly assigned to receive an i.m. injection of meloxicam (0.5 mg/kg) or a placebo. All calves were given a lidocaine cornual nerve block (5 mL per horn). Treatments and nerve blocks were administered 10 min before cautery dehorning. Continuous sampling of behavior was performed during five 1-h intervals using video recordings, and total daily activity was monitored using an accelerometer. A pain sensitivity test was administered with a pressure algometer, and feed and water intakes were recorded daily. Calves were sham-dehorned 24 h before actual dehorning to establish baseline values, and all variables were assessed at the same times following dehorning and sham dehorning for up to 48 h post-dehorning. Meloxicam-treated calves displayed less ear flicking during the 44 h following dehorning (increases of 4.29+/-1.10 and 1.31+/-0.66 ear flicks/h in the first 24 h, and increases of 3.27+/-0.89 and 0.55+/-0.50 ear flicks/h during the second 24 h, for control and meloxicam calves, respectively) and less head shaking during the first 9 h following dehorning (increase of 2.53+/-0.54 and 0.85+/-0.46 headshakes/h over baseline for control and meloxicam, respectively). Meloxicam-treated calves were less active than controls during the first 5 h following dehorning (activity 34.1+/-3.2 and 30.6+/-2.6 for control and meloxicam, respectively) and displayed less sensitivity to pressure algometry 4 h after dehorning (pressure tolerance of 1.62+/-0.13 kg of force and 2.13+/-0.15 kg of force for control and meloxicam calves, respectively). Changes in behavior suggest that meloxicam was effective for reducing post-surgical pain and distress associated with calf dehorning.


title: A modified gait generator for humanoid robots based on height compensation of center of mass

abstract: This paper presents a modified gait generation method based on height compensation of center of mass (HCCOM) for biped humanoid robots. The method combines natural gait plan (NGP) and 3-D linear inverted pendulum model (3D-LIPM). By analyzing the human natural gait, the HCCOM considers the effect of swinging leg for COM height, so the hip height is no longer fixed and re-planned as a periodic movement. We divide the walk gait into four stages and indicate the process of gait generation. For the test platform, we develop IPR-I robot, through the simulation and experimental analysis, the HCCOM can effectively solve the swinging leg's effect on COM height, get stable and practical gait, reduce effect of the model inaccuracy for the 3D-LIPM.


title: Behavioral Representation Multi-Tasking and Cognitive Workload in an ACT-R Model of a Simplified Air Traffic Control Task.

abstract: We present a human performance model of operator control of a simplified air traffic control task developed under the Agent-based Modeling and Behavior Representation program (AMBR). The model was implemented using the ACT-R architecture of cognition. Using a well-developed cognitive architecture provided a number of benefits: 1 . The reuse of common design patterns, such as unit task decomposition and retrieval/computation dichotomy, greatly simplified and accelerated model development. 2. The model inherited parameter values from previous models and required no fine-tuning of parameters. 3. The architectural learning mechanisms provided automatic learning of situations. The resulting model was quite simple, consisting of only five declarative chunks of information and 36 production rules, while providing a highly accurate model of human performance. The model matched a wide range of performance measures, including amount and type of errors, response latency and choice percentages. Performance variability is a fundamental aspect of human behavior in complex task. The model accounted not only for the average but also for the distribution of performance through the fundamentally stochastic nature of the architecture, amplified by the interaction with the dynamic simulation environment. Although ACT-R is a goal-directed architecture, the model provided a straightforward account of multi-tasking behavior through the use of interruptions triggered by the onset of messages. The model also provided a theory of cognitive workload based on architectural primitives. The port of the model to the High Level Architecture (HLA) was relatively simple and straightforward. Providing a stochastic model of behavior, however, put strong demands on the efficiency of the simulation. The implications of such demands for HLA-enabled human performance models are discussed.


title: Learning data augmentation policies using augmented random search

abstract: Previous attempts for data augmentation are designed manually, and the augmentation policies are dataset-specific. Recently, an automatic data augmentation approach, named AutoAugment, is proposed using reinforcement learning. AutoAugment searches for the augmentation polices in the discrete search space, which may lead to a sub-optimal solution. In this paper, we employ the Augmented Random Search method (ARS) to improve the performance of AutoAugment. Our key contribution is to change the discrete search space to continuous space, which will improve the searching performance and maintain the diversities between sub-policies. With the proposed method, state-of-the-art accuracies are achieved on CIFAR-10, CIFAR-100, and ImageNet (without additional data). Our code is available at this https URL.


title: Environmental measures for hotels' environmental management systems: ISO 14001

abstract: Purpose – Implementation of environmental management systems (EMS) in hotels is gaining popularity world‐wide. ISO 14001, which is the only certifying document in the ISO 14000 series, provides guidelines to set up an EMS. However, there is a paucity of information about the actual environmental measures implemented in ISO 14001 certified hotels. Therefore, the purpose of this paper is to identify and generalize the environmental measures undertaken by studied hotels and to evaluate the performance of these environmental measures.Design/methodology/approach – Three case studies were carried out to identify the green measures undertaken in ISO 14001‐certified hotels. Both qualitative and quantitative data were collected and analyzed. Then, regression analysis was conducted to examine the relationship between monthly utilities input (electricity, gas, fuel oil and water) and activity parameters.Findings – A total of 113 measures were identified, nearly half of which concern energy conservation. Results of m...


title: Moving Target Defense: A Symbiotic Framework for AI & Security

abstract: Modern day technology has found its way into every aspect of our lives-- be it the server storing our social information, the hand-held smartphones, the home security systems or a remotely monitored pacemaker. Unfortunately, this also increases the opportunity for agents with malicious intent to violate the privacy, availability or integrity of these applications. In fact, with the advancement of Artificial Intelligence (AI) and faster hardware, the process of finding and exploiting vulnerabilities is no longer as time-consuming as before. Moving Target Defense (MTD) is emerging as an effective technique in addressing these security concerns. This technique, as used by the cyber security community, however, does not incorporate the dynamics of a multi-agent system between an attacker and defender, resulting in sub-optimal behavior. My study of such systems in a multi-agent context helps to enhance the security of MTD systems and proposes a list of challenges for the AI community. Furthermore, borrowing the example of MTD systems from the cyber security community, we can address some security concerns of the present day AI algorithms. In this abstract, I describe my research work that uses AI for enhancing security of a multi-agent MTD system and highlight research avenues in using MTD for enhancing security of present AI algorithms.


title: A New Technique to Design Circularly Polarized Microstrip Antenna by Fractal Defected Ground Structure

abstract: A new technique to design single-feed circularly polarized (CP) microstrip antenna is proposed. The CP radiation is obtained by adjusting the dimension of the etched fractal defected ground structure (FDGS) in the ground plane. Parameter studies of the FDGS are given to illustrate the way to achieve CP radiation. The CP microstrip antennas with the second and third iterative FDGS are fabricated and measured. The measured 10-dB return-loss bandwidth of the CP microstrip antenna is about 30 MHz (1.558 to 1.588 GHz), while its 3-dB axial-ratio bandwidth is 6 MHz (1.572 to 1.578 GHz). The gain across the CP band is between 1.7 and 2.2 dBic.


title: Hotel Revenue Management – A Critical Literature Review

abstract: The paper presents a critical literature review of the main concepts of hotel revenue management (RM) and current state of theoretical research. It emphasizes on the different directions of hotel RM research and focuses comprehensively on the elements of the hotel RM system and the stages of RM process. Special attention is paid to the different hotel RM centres, pricing and non-pricing RM tools, forecasting methods, the RM software, RM team and ethical considerations as they play a central role in the RM practice. Finally, the article outlines future research perspectives and discloses potential evolution of RM in future.


title: An overview of the reliability prediction related aspects of high power IGBTs in wind power applications

abstract: Abstract Reliability is becoming more and more important as the size and number of installed Wind Turbines (WTs) increases. Very high reliability is especially important for offshore WTs because the maintenance and repair of such WTs in case of failures can be very expensive. WT manufacturers need to consider the reliability aspect when they design new power converters. By designing the power converter considering the reliability aspect the manufacturer can guarantee that the end product will ensure high availability. This paper represents an overview of the various aspects of reliability prediction of high power Insulated Gate Bipolar Transistors (IGBTs) in the context of wind power applications. At first the latest developments and future predictions about wind energy are briefly discussed. Next the dominant failure mechanisms of high power IGBTs are described and the most commonly used lifetime prediction models are reviewed. Also the concept of Accelerated Life Testing (ALT) is briefly reviewed.


title: The Cerebellum and Neurodevelopmental Disorders

abstract: Cerebellar dysfunction is evident in several developmental disorders, including autism, attention deficit-hyperactivity disorder (ADHD), and developmental dyslexia, and damage to the cerebellum early in development can have long-term effects on movement, cognition, and affective regulation. Early cerebellar damage is often associated with poorer outcomes than cerebellar damage in adulthood, suggesting that the cerebellum is particularly important during development. Differences in cerebellar development and/or early cerebellar damage could impact a wide range of behaviors via the closed-loop circuits connecting the cerebellum with multiple cerebral cortical regions. Based on these anatomical circuits, behavioral outcomes should depend on which cerebro-cerebellar circuits are affected. Here, we briefly review cerebellar structural and functional differences in autism, ADHD, and developmental dyslexia, and discuss clinical outcomes following pediatric cerebellar damage. These data confirm the prediction that abnormalities in different cerebellar subregions produce behavioral symptoms related to the functional disruption of specific cerebro-cerebellar circuits. These circuits might also be crucial to structural brain development, as peri-natal cerebellar lesions have been associated with impaired growth of the contralateral cerebral cortex. The specific contribution of the cerebellum to typical development may therefore involve the optimization of both the structure and function of cerebro-cerebellar circuits underlying skill acquisition in multiple domains; when this process is disrupted, particularly in early development, there could be long-term alterations of these neural circuits, with significant impacts on behavior.


title: Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora

abstract: Unsupervised word segmentation (UWS) can provide domain-adaptive segmentation for statistical machine translation (SMT) without annotated data, and bilingual UWS can even optimize segmentation for alignment. Monolingual UWS approaches of explicitly modeling the probabilities of words through Dirichlet process (DP) models or Pitman-Yor process (PYP) models have achieved high accuracy, but their bilingual counterparts have only been carried out on small corpora such as basic travel expression corpus (BTEC) due to the computational complexity. This paper proposes an efficient unified PYP-based monolingual and bilingual UWS method. Experimental results show that the proposed method is comparable to supervised segmenters on the in-domain NIST OpenMT corpus, and yields a 0.96 BLEU relative increase on NTCIR PatentMT corpus which is out-of-domain.


title: Automatic Chord Estimation from Audio: A Review of the State of the Art

abstract: In this overview article, we review research on the task of Automatic Chord Estimation (ACE). The major contributions from the last 14 years of research are summarized, with detailed discussions of the following topics: feature extraction, modeling strategies, model training and datasets, and evaluation strategies. Results from the annual benchmarking evaluation Music Information Retrieval Evaluation eXchange (MIREX) are also discussed as well as developments in software implementations and the impact of ACE within MIR. We conclude with possible directions for future research.


title: Basic running test of the cylindrical tracked vehicle with sideways mobility

abstract: In this paper, the basic running performance of the cylindrical tracked vehicle with sideways mobility is presented. The crawler mechanism is of circular cross-section and has active rolling axes at the center of the circles. Conventional crawler mechanisms can support massive loads, but cannot produce sideways motion. Additionally, previous crawler edges sink undesirably on soft ground, particularly when the vehicle body is subject to a sideways tilt. The proposed design solves these drawbacks by adopting a circular cross-section crawler. A prototype. Basic motion experiments with confirm the novel properties of this mechanism: sideways motion and robustness against edge-sink.


title: Value-based requirements engineering: exploring innovative e-commerce ideas

abstract: Innovative e-commerce ideas are characterised by commercial products yet unknown to the market, enabled by information technology such as the Internet and technologies on top of it. How to develop such products is hardly known. We propose an interdisciplinary approach, e3-value, to explore an innovative e-commerce idea with the aim of understanding such an idea thoroughly and evaluating it for potential profitability. Our methodology exploits a requirements engineering way of working, but employs concepts and terminology from business science, marketing and axiology. It shows how to model business requirements and improve business–IT alignment, in sophisticated multi-actor value constellations that are common in electronic commerce. In addition to the e3-value approach methodology, we also present the action research-based development of our methodology, by using one of the longitudinal projects we carried out in the field of online news article provisioning.


348 Book Reviews Nonlinear Time Series: Nonparametric and Parametric Methods.


title: When and why do retrieval attempts enhance subsequent encoding?

abstract: Unsuccessful retrieval attempts can enhance subsequent encoding and learning. In three experiments, subjects either attempted to retrieve word pairs prior to studying them (e.g., attempting to recall tide–? before studying tide–beach) or did not attempt retrieval and retention of the studied targets was assessed on a subsequent cued recall test. Experiment 1 showed that attempting retrieval enhanced subsequent encoding and recall relative to not attempting retrieval when the word pairs were semantically related, but not when the pairs were unrelated. In Experiment 2, studying a different word pair prior to the correct pair (e.g., studying tide–wave prior to tide–beach) did not produce the same effect as attempting retrieval prior to studying. Constraining retrieval to a particular candidate word prior to study (e.g., recalling tide–wa__ before studying tide–beach) produced a negative effect on subsequent recall. Experiment 3 showed that attempting retrieval did not enhance encoding when a brief delay occurred between the retrieval attempt and the subsequent study trial. The results support the idea that a search set of candidates related to the retrieval cue is activated during retrieval and that this retrieval-specific activation can enhance subsequent encoding of those candidates.


title: Self-eating and self-killing: crosstalk between autophagy and apoptosis

abstract: The relationship between autophagy and apoptosis is complex; autophagy constitutes an adaptive stress response that avoids cell death and suppresses apoptosis under certain circumstances, whereas in other cellular settings it contributes to the demise of cells through an alternative cell-death pathway.


title: Anomaly Detection from Log Files Using Data Mining Techniques

abstract: Log files are created by devices or systems in order to provide information about processes or actions that were performed. Detailed inspection of security logs can reveal potential security breaches and it can show us system weaknesses. In our work we propose a novel anomaly-based detection approach based on data mining techniques for log analysis. Our approach uses Apache Hadoop technique to enable processing of large data sets in a parallel way. Dynamic rule creation enables us to detect new types of breaches without further human intervention. Overall error rates of our method are below 10%.


title: A double-auction mechanism for mobile data-offloading markets

abstract: The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Mobile data offloading through third-party Wi-Fi or femtocell access points (APs) can significantly alleviate the cellular congestion and enhance user quality of service (QoS), without requiring costly and time-consuming infrastructure investments. This solution has substantial benefits both for the mobile network operators (MNOs) and the mobile users, but comes with unique technical and economic challenges that must be jointly addressed. In this paper, we consider a market where MNOs lease APs that are already deployed by residential users for the offloading purpose. We assume that each MNO can employ multiple APs, and each AP can concurrently serve traffic from multiple MNOs. We design an iterative double-auction mechanism that ensures the efficient operation of the market by maximizing the differences between the MNOs' offloading benefits and APs' offloading costs. The proposed scheme takes into account the particular characteristics of the wireless network, such as the coupling of MNOs' offloading decisions and APs' capacity constraints. Additionally, it does not require full information about the MNOs and APs and creates non-negative revenue for the market broker.


Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields


Product and Service Performance Model for Information Quality: An Update.


An RC-triggered ESD clamp for high-voltage BCD CMOS processes


title: The Effect of Pinned Photodiode Shape on Time-of-Flight Demodulation Contrast

abstract: An empirical investigation on improving the pinned photodiode (PPD) demodulation contrast by tailoring the geometry of the device is presented. Results of this TCAD simulation-based study are used to develop a structure especially suited for time-of-flight applications. In order to obtain a fair comparison between various PPD shapes, a square structure is adopted as a benchmark and all subsequent PPD geometries use the same process parameters. Five different PPD shapes are compared: 1) nominal square-shaped PPD; 2) triangular PPD; 3) constant-field PPD; 4) L-shaped constant-field PPD; and 5) proposed PPD. Device physics simulations are undertaken and the speed of each structure is evaluated on the basis of its demodulation contrast. It is shown that triangular and constant-field PPDs can provide significant improvement compared with a conventional square-shaped PPD, however they still lack effective lateral charge transfer in the final electron sorting stage. The final PPD proposed in this paper achieves this with a tailored PPD shape and doping gradient. In addition, the transfer gates are placed close to one another to make use of gate-induced fringe fields and thus improve the speed of electron sorting. Using these techniques, a PPD demodulation contrast of 61% is obtained at a frequency of 100 MHz, which is comparable to the contrast achieved in state-of-the-art photogate-based designs.


title: Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS)

abstract: The Automatic Short Answer Scoring (ASAS) system is one of the tools that can be used to conduct assessment process on e-learning system. One of the methods applied in the ASAS system is a method for measuring similarities between the reference and student answers. There are two issues to be considered in the assessment process using this method. First, this method should be able to provide a variety of reference answers that can handle the diversity of student answers. Secondly, this method should be able to provide an accurate sentence similarity between the reference answers and student answers. Therefore, two methods are proposed to solve both problems. The first method is to generate a variety of reference answers automatically using Maximum Marginal Relevance (MMR) method, which obtains an accuracy of 91.95%. The second method is to measure accurately sentence similarity between student answers and reference answers that have significantly different length using GAN-LCS. The performance of the proposed method shows an improvement of the Root Mean Square Error (RMSE) value of 0.884 and a correlation value of 0.468.


title: On the feasibility of hybrid Battery/Ultracapacitor Energy Storage Systems for next generation shipboard power systems

abstract: This paper explores a Battery/Ultracapacitor Energy Storage System (BUCESS) for navy application. A new configuration of the battery and ultracapacitor combined system is introduced for propulsion system and pulse power loads. A dual active bridge (DAB) topology is selected to control the bidirectional power flow through phase shifting for charging and discharging batteries and ultracapacitors. High-frequency switching devices are selected to achieve dc-dc conversion at high voltage and high power levels. A 500V–1kV BUCESS is designed and analyzed to investigate 100kW transmission for batteries and 1MW for ultracapacitors both in charging and discharging modes.


title: Examination Findings in Legally Confirmed Child Sexual Abuse: It's Normal to be Normal

abstract: Background. Studies of alleged victims of child sexual abuse vary greatly in the reported fre- quency of physical findings based on differences in def- inition of abuse and of "findings." This study was de- signed to determine the frequency of abnormal findings in a population of children with legal confirmation of sexual abuse, using a standardized classification system for colposcopic photographic findings. Methods. Case files and colposcopic photographs of 236 children with perpetrator conviction for sexual abuse, were reviewed. The photos were reviewed blindly by a team member other than the examiner, and specific ana- tomical findings were noted and classified as normal to abnormal on a scale of 1 to 5. Historical and behavioral information, as well as legal outcome was recorded, and all data entered into a dBase III program. Correlations were sought between abnormal findings and other variables. Results. The mean age of the patients was 9.0 years (range 8 months to 17 years, 11 months), with 63% report- ing penile-genital contact. Genital examination findings in girls were normal in 28%, nonspecific in 49%, suspi- cious in 9%, and abnormal in 14% of cases. Abnormal anal findings were found in only 1% of patients. Using discriminant analysis, the two factors which significantly correlated with the presence of abnormal genital find- ings in girls were the time since the last incident, and a history of blood being reported at the time of the molest. Conclusions. Abnormal genital findings are not com- mon in sexually abused girls, based on a standardized dassification system. More emphasis should be placed on documenting the child's description of the molesta- tion, and educating prosecutors that, for children alleg- ing abuse: "It's normal to be normal." Pediatrics 1994;94:


title: Simultaneous learning of hierarchy and primitives for complex robot tasks

abstract: We present a new interaction paradigm for robot learning from demonstration, called simultaneous learning of hierarchy and primitives (SLHAP), in which information about hierarchy and primitives is naturally interleaved in a single, coherent demonstration session. A key innovation in the new paradigm is the human demonstrator’s narration of primitives as he executes them, which allows the system to identify the boundaries between primitives. Hierarchy is represented using hierarchical task networks; motion planning constraints on the primitives are represented using task space regions. We implemented SLHAP on an autonomous robot and produced an interaction video illustrating its effectiveness learning a complex task with five levels of hierarchy and eight types of primitives. The underlying algorithms which make SLHAP possible are described and evaluated.


title: Learning Gender-Neutral Word Embeddings

abstract: Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications. However, embeddings trained on human-generated corpora have been demonstrated to inherit strong gender stereotypes that reflect social constructs. To address this concern, in this paper, we propose a novel training procedure for learning gender-neutral word embeddings. Our approach aims to preserve gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence. Based on the proposed method, we generate a Gender-Neutral variant of GloVe (GN-GloVe). Quantitative and qualitative experiments demonstrate that GN-GloVe successfully isolates gender information without sacrificing the functionality of the embedding model.


title: Continuum Delta Robot: a Novel Translational Parallel Robot with Continuum Joints

abstract: Researches on continuum robots thrive due to many advantages such as design compactness and motion dexterity. Among the recent advances, it is well noted the proposal of Parallel Continuum Robot (PCR) whose legs are made from elastic rods. The legs undergo deformations along their entire lengths and sophisticated mechanics is used to describe their shapes and the kinematics. Following a different approach, this paper proposes to design parallel robots with continuum joints. The continuum joints would assume constant curvature bending and hence produce relatively simple kinematics. With the bending ranges achievable beyond 90°, a parallel robot with continuum joints, instead of universal or spherical joints, can realize larger workspace. As a particular demonstrative example, a Continuum Delta Robot (CDR) is proposed. Each of the CDR's legs consists of two coupled continuum joints which inherently realize translational movements of the end effector. The design concept, kinematics, system description and experimental characterizations are presented. From the presented Continuum Delta Robot, more parallel robots with continuum joints can be proposed and new design methodology can be developed.


Triticale: a promising addition to the world's cereal grains.


An Analysis of Cloud Computing and its Security Issues


title: Reliability Comparison of ISSG Oxide and HTO as Tunnel Dielectric in 3-D–SONOS Applications

abstract: The reliability (endurance and retention) of tunnel oxide (grown and deposited oxide) of 3-D–SONOS devices is compared. Devices with grown tunnel oxide show better initial oxide quality, better fresh retention, and more robust endurance characteristics. Both devices show similar cycling degradation trend. The worse postcycling retention is due to tunnel oxide degradation leading to a fast initial loss of charge stored in generated tunnel oxide defects and a higher trap-assisted tunneling rate.


title: Effects of gamification on participation and data quality in a real-world market research domain

abstract: Gamification has become an increasingly popular way to improve user engagement and motivation, but there is currently a lack of empirical research to demonstrate that increased gamification provides these benefits. To help address this problem we designed three versions of a gamified market research survey and tested them alongside the established industry standard in a study of over 600 participants. We also highlight examples where game elements compromise respondent data, and provide design solutions that correct the problem without losing the motivational benefits of gamification.


title: Daily Mindful Responding Mediates the Effect of Meditation Practice on Stress and Mood: The Role of Practice Duration and Adherence

abstract: Objective ::: Although meditation practice is an important component of many mindfulness-based interventions (MBIs), empirical findings of its effects on psychological functioning are mixed and the mechanisms for the effects remain unclear. Responding with mindfulness (i.e., returning one's attention back to a nonjudgmental, present-oriented awareness) is a fundamental skill practiced in meditations. With repeated meditation practice, this skill is thought to become internalized and be applied to one's daily life. We thus hypothesized that the extent to which individuals responded to daily events with mindfulness would mediate the effects of meditation practice (instance, duration, and adherence to instructions) on psychological well-being. ::: ::: Method ::: Using a daily diary methodology, we tracked the meditation practice, use of mindful responding during the day, and psychological outcomes (perceived stress, negative and positive affect) of 117 mindfulness-based stress reduction program participants. ::: ::: Results ::: We found that on days when participants meditated, they responded with greater mindfulness to daily events, which accounted for the beneficial effects of meditating on psychological outcomes. Furthermore, findings suggest that on meditation days, longer and more closely adhered meditation practices were independently associated with increases in mindful responding, which in turn were associated with better psychological outcomes. ::: ::: Conclusion ::: These results suggest that regular, longer, and more closely adhered meditation practice is an important component of MBIs, in part because it leads to responding more mindfully in daily life, which promotes well-being.


title: Deep Residual Learning and PDEs on Manifold

abstract: In this paper, we formulate the deep residual network (ResNet) as a control problem of transport equation. In ResNet, the transport equation is solved along the characteristics. Based on this observation, deep neural network is closely related to the control problem of PDEs on manifold. We propose several models based on transport equation and Hamilton-Jacobi equation. The discretization of these PDEs on point cloud is also discussed.


Alienation and the Use of Psychogenic Drugs Among Adolescents in Nigeria: A Report.


title: Counting People With Low-Level Features and Bayesian Regression

abstract: An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed. Instead, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic-texture motion model. A set of holistic low-level features is extracted from each segmented region, and a function that maps features into estimates of the number of people per segment is learned with Bayesian regression. Two Bayesian regression models are examined. The first is a combination of Gaussian process regression with a compound kernel, which accounts for both the global and local trends of the count mapping but is limited by the real-valued outputs that do not match the discrete counts. We address this limitation with a second model, which is based on a Bayesian treatment of Poisson regression that introduces a prior distribution on the linear weights of the model. Since exact inference is analytically intractable, a closed-form approximation is derived that is computationally efficient and kernelizable, enabling the representation of nonlinear functions. An approximate marginal likelihood is also derived for kernel hyperparameter learning. The two regression-based crowd counting methods are evaluated on a large pedestrian data set, containing very distinct camera views, pedestrian traffic, and outliers, such as bikes or skateboarders. Experimental results show that regression-based counts are accurate regardless of the crowd size, outperforming the count estimates produced by state-of-the-art pedestrian detectors. Results on 2 h of video demonstrate the efficiency and robustness of the regression-based crowd size estimation over long periods of time.


title: Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining

abstract: Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. When unlicensed users opportunistically utilize spectrum holes, prediction models that infer the availability of spectrum holes can help to improve the spectrum extraction rate and reduce the collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims at identifying frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users are able to utilize spectrum holes aggressively without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real-world Wi-Fi and personal communication service (PCS) activities, we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved. Further, we extend the three-state PPPM to an N-state PPPM to predict the duration of high/low utilization in a channel. The frequent patterns of channel utilization duration are critical in optimizing channel switch strategies. The high prediction accuracy is validated with data collected in the paging bands.


title: Image processing based ann with Bayesian regularization learning algorithm for classification of wheat grains

abstract: In this paper, an image processing technique (IPT) based artificial neural network (ANN) model using Bayesian regularization (BR) learning algorithm is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the ANN-BR model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then ANN-BR model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The ANN-BR model numerically calculate the outputs with mean absolute error (MAE) of 0.017 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based ANN-BR model can be successfully applied to classification of wheat grains.


title: Multimodal Analysis and Prediction of Persuasiveness in Online Social Multimedia

abstract: Our lives are heavily influenced by persuasive communication, and it is essential in almost any type of social interaction from business negotiation to conversation with our friends and family. With the rapid growth of social multimedia websites, it is becoming ever more important and useful to understand persuasiveness in the context of social multimedia content online. In this article, we introduce a newly created multimedia corpus of 1,000 movie review videos with subjective annotations of persuasiveness and related high-level characteristics or attributes (e.g., confidence). This dataset will be made freely available to the research community. We designed our experiments around the following five main research hypotheses. First, we study if computational descriptors derived from verbal and nonverbal behavior can be predictive of persuasiveness. We further explore combining descriptors from multiple communication modalities (acoustic, verbal, para-verbal, and visual) for predicting persuasiveness and compare with using a single modality alone. Second, we investigate how certain high-level attributes, such as credibility or expertise, are related to persuasiveness and how the information can be used in modeling and predicting persuasiveness. Third, we investigate differences when speakers are expressing a positive or negative opinion and if the opinion polarity has any influence in the persuasiveness prediction. Fourth, we further study if gender has any influence in the prediction performance. Last, we test if it is possible to make comparable predictions of persuasiveness by only looking at thin slices (i.e., shorter time windows) of a speaker's behavior.


title: Robust radial basis function neural networks

abstract: Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function, However, it still suffers from two major problems. First, it is difficult to use Gaussian functions to approximate constant values. If a function has nearly constant values in some intervals, the RBF network will be found inefficient in approximating these values. Second, when the training patterns incur a large error, the network will interpolate these training patterns incorrectly. In order to cope with these problems, an RBF network is proposed in this paper which is based on sequences of sigmoidal functions and a robust objective function. The former replaces the Gaussian functions as the basis function of the network so that constant-valued functions can be approximated accurately by an RBF network, while the latter is used to restrain the influence of large errors. Compared with traditional RBF networks, the proposed network demonstrates the following advantages: (1) better capability of approximation to underlying functions; (2) faster learning speed; (3) better size of network; (4) high robustness to outliers.


title: Relationships between Ground Reaction Force Impulse and Kinematics of Sprint-Running Acceleration

abstract: The literature contains some hypotheses regarding the most favorable ground reaction force (GRF) for sprint running and how it might be achieved. This study tested the relevance of these hypotheses to the acceleration phase of a sprint, using GRF impulse as the GRF variable of interest. Thirty-six athletes performed maximal-effort sprints from which video and GRF data were collected at the 16-m mark. Associations between GRF impulse (expressed relative to body mass) and various kinematic measures were explored with simple and multiple linear regressions and paired t-tests. The regression results showed that relative propulsive impulse accounted for 57% of variance in sprint velocity. Relative braking impulse accounted for only 7% of variance in sprint velocity. In addition, the faster athletes tended to produce only moderate magnitudes of relative vertical impulse. Paired t-tests revealed that lower magnitudes of relative braking impulse were associated with a smaller touchdown distance (p < 0.01) and a more active touchdown (p < 0.001). Also, greater magnitudes of relative propulsive impulse were associated with a high mean hip extension velocity of the stance limb (p < 0.05). In conclusion, it is likely that high magnitudes of propulsion are required to achieve high acceleration. Although there was a weak trend for faster athletes to produce lower magnitudes of braking, the possibility of braking having some advantages could not be ruled out. Further research is required to see if braking, propulsive, and vertical impulses can be modified with specific training. This will also provide insight into how a change in one GRF component might affect the others.


title: A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

abstract: Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-ofthe-art baseline methods.


title: Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions

abstract: Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc. Competing frameworks for building these networks such as TensorFlow, Chainer, CNTK, Torch/PyTorch, Caffe1/2, MXNet and Theano, explore different tradeoffs between usability and expressiveness, research or production orientation and supported hardware. They operate on a DAG of computational operators, wrapping high-performance libraries such as CUDNN (for NVIDIA GPUs) or NNPACK (for various CPUs), and automate memory allocation, synchronization, distribution. Custom operators are needed where the computation does not fit existing high-performance library calls, usually at a high engineering cost. This is frequently required when new operators are invented by researchers: such operators suffer a severe performance penalty, which limits the pace of innovation. Furthermore, even if there is an existing runtime call these frameworks can use, it often doesn't offer optimal performance for a user's particular network architecture and dataset, missing optimizations between operators as well as optimizations that can be done knowing the size and shape of data. Our contributions include (1) a language close to the mathematics of deep learning called Tensor Comprehensions offering both imperative and declarative styles, (2) a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning DAG into a CUDA kernel with delegated memory management and synchronization, also providing optimizations such as operator fusion and specialization for specific sizes, (3) a compilation cache populated by an autotuner. [Abstract cutoff]


Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts


title: Toward a secure and usable cloud-based password manager for web browsers

abstract: Web users are confronted with the daunting challenges of creating, remembering, and using more and more strong passwords than ever before in order to protect their valuable assets on different websites. Password manager, particularly Browser-based Password Manager (BPM), is one of the most popular approaches designed to address these challenges by saving users' passwords and later automatically filling the login forms on behalf of users. Fortunately, all the five most popular Web browsers have provided password managers as a useful built-in feature. In this paper, we uncover the vulnerabilities of existing BPMs and analyze how they can be exploited by attackers to crack users' saved passwords. Moreover, we propose a novel Cloud-based Storage-Free BPM (CSF-BPM) design to achieve a high level of security with the desired confidentiality, integrity, and availability properties. We have implemented a CSF-BPM system into Firefox and evaluated its correctness, performance, and usability. Our evaluation results and analysis demonstrate that CSF-BPM can be efficiently and conveniently used. We believe CSF-BPM is a rational design that can also be integrated into other popular browsers to make the online experience of Web users more secure, convenient, and enjoyable.


title: Generating Aspect-oriented Multi-Document Summarization with Event-aspect model

abstract: In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on Rouge metric demonstrates the effectiveness and advantages of our method.


title: Interconnecting Fog computing and microgrids for greening IoT

abstract: The Internet of things (IoT) is hailed as the next big phase in the evolution of the Internet. IoT devices are rapidly proliferating, owing to widespread adoption by industries in various sectors. Unlike security and privacy concerns, the energy consumption of the IoT and its applications has received little attention by the research community. This paper explores different options for deploying energy-efficient IoT applications. Specifically, we evaluate the use of a combination of Fog computing and microgrids for reducing the energy consumption of IoT applications. First, we study the energy consumption of different types of IoT applications (such as IoT applications with differing traffic generation or computation) running from both Fog and Cloud computing. Next, we consider the role of local renewable energy provided by microgrids, and local weather forecasting, along with Fog computing. To evaluate our proposal, energy consumption modeling, practical experiments and measurements were performed. The results indicate that the type of IoT application, the availability of local renewable energy, and weather forecasting all influence how a system makes dynamic decisions in terms of saving energy.


title: SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.


title: Central Pattern Generators augmented with virtual model control for quadruped rough terrain locomotion

abstract: We present a modular controller for quadruped locomotion over unperceived rough terrain. Our approach is based on a computational Central Pattern Generator (CPG) model implemented as coupled nonlinear oscillators. Stumbling correction reflex is implemented as a sensory feedback mechanism affecting the CPG. We augment the outputs of the CPG with virtual model control torques responsible for posture control. The control strategy is validated on a 3D forward dynamics simulated quadruped robot platform of about the size and weight of a cat. To demonstrate the capabilities of the proposed approach, we perform locomotion over unperceived uneven terrain and slopes, as well as situations facing external pushes.


title: On the Weakness of Fully Homomorphic Encryption

abstract: Fully homomorphic encryption (FHE) allows anyone to perform computations on encrypted data, despite not having the secret decryption key. Since the Gentry's work in 2009, the primitive has interested many researchers. In this paper, we stress that any computations performed on encrypted data are constrained to the encrypted domain (finite fields or rings). This restriction makes the primitive useless for most computations involving common arithmetic expressions and relational expressions. It is only applicable to the computations related to modular arithmetic. We want to reaffirm that cryptography uses modular arithmetic a lot in order to obscure and dissipate the redundancies in a plaintext message, not to perform any numerical calculations. We think it might be an overstated claim that FHE is of great importance to client-server computing or cloud computing.


title: Reactance-domain ESPRIT algorithm for a hexagonally shaped seven-element ESPAR antenna

abstract: A direction-of-arrival (DoA) method that combines the reactance-domain (RD) technique and the ESPRIT algorithm is proposed for use with the 7-element electronically steerable parasitic array radiator (ESPAR) for the estimation of noncoherent sources. Simulations show that the method could resolve up to three incoming signals with an estimation performance that depends on the signal's angle of arrival. Moreover, the method is compared with the Cramer-Rao lower bound (CRB) and the MUSIC asymptotic error variance, both modified for the RD technique. Numerical comparison between this lower bound and the MUSIC algorithm confirmed that the proposed method can achieve the CRB and provide high-precision DoA estimation with a level of performance that is sufficient for many DoA finding applications. The proposed method could be demonstrated by means of experiments on DOA estimation conducted in an anechoic chamber.


title: Constructive procedures to solve 2-dimensional bin packing problems with irregular pieces and guillotine cuts

abstract: This paper presents an approach for solving a new real problem in cutting and packing. At its core is an innovative mixed integer programme model that places irregular pieces and defines guillotine cuts. The two-dimensional irregular shape bin packing problem with guillotine constraints arises in the glass cutting industry, for example, the cutting of glass for conservatories. Almost all cutting and packing problems that include guillotine cuts deal with rectangles only, where all cuts are orthogonal to the edges of the stock sheet and a maximum of two angles of rotation are permitted. The literature tackling packing problems with irregular shapes largely focuses on strip packing i.e. minimizing the length of a single fixed width stock sheet, and does not consider guillotine cuts. Hence, this problem combines the challenges of tackling the complexity of packing irregular pieces with free rotation, guaranteeing guillotine cuts that are not always orthogonal to the edges of the stock sheet, and allocating pieces to bins. To our knowledge only one other recent paper tackles this problem. We present a hybrid algorithm that is a constructive heuristic that determines the relative position of pieces in the bin and guillotine constraints via a mixed integer programme model. We investigate two approaches for allocating guillotine cuts at the same time as determining the placement of the piece, and a two phase approach that delays the allocation of cuts to provide flexibility in space usage. Finally we describe an improvement procedure that is applied to each bin before it is closed. This approach improves on the results of the only other publication on this problem, and gives competitive results for the classic rectangle bin packing problem with guillotine constraints.


title: Memory hazard functions: A vehicle for theory development and test.

abstract: A framework is developed to rigorously test an entire class of memory retention functions by examining hazard properties. Evidence is provided that the memory hazard function is not monotonically decreasing. Yet most of the proposals for retention functions, which have emerged from the psychological literature, imply that memory hazard is monotonically decreasing over the entire temporal range. Furthermore, the few remaining proposals, that do not have monotonically decreasing hazard, have difficulty fitting data over both short-term and long-term intervals. A new 2-trace hazard model is developed that successfully circumvents these difficulties. This new model is used to account for the size of memory span and the time course of proactive and retroactive interference effects. The model can fit the retention characteristics of H. M., the famous amnesic patient, as well as normal experimental participants. The model is also used to account for the time course of the misinformation effect.


title: Learning Invariant Features Using Subspace Restricted Boltzmann Machine

abstract: The subspace restricted Boltzmann machine (subspaceRBM) is a third-order Boltzmann machine where multiplicative interactions are between one visible and two hidden units. There are two kinds of hidden units, namely, gate units and subspace units. The subspace units reflect variations of a pattern in data and the gate unit is responsible for activating the subspace units. Additionally, the gate unit can be seen as a pooling feature. We evaluate the behavior of subspaceRBM through experiments with MNIST digit recognition task and Caltech 101 Silhouettes image corpora, measuring cross-entropy reconstruction error and classification error.


Myanmar Spell Checker


title: Social commerce constructs and consumer's intention to buy

abstract: This research investigates the new stream in e-commerce.The research uses SEM-PLS to analysis the proposed model.The model explains social commerce constructs and its influence on intention to buy and trust. Social commerce is a new development in e-commerce generated by the use of social media to empower customers to interact on the Internet. The recent advancements in ICTs and the emergence of Web 2.0 technologies along with the popularity of social media and social networking sites have seen the development of new social platforms. These platforms facilitate the use of social commerce. Drawing on literature from marketing and information systems (IS) the author proposes a new model to develop our understanding of social commerce using a PLS-SEM methodology to test the model. Results show that Web 2.0 applications are attracting individuals to have interactions as well as generate content on the Internet. Consumers use social commerce constructs for these activities, which in turn increase the level of trust and intention to buy. Implications, limitations, discussion, and future research directions are discussed at the end of the paper.


title: Bundle Optimization for Multi-aspect Embedding

abstract: Understanding semantic similarity among images is the core of a wide range of computer vision applications. An important step towards this goal is to collect and learn human perceptions. Interestingly, the semantic context of images is often ambiguous as images can be perceived with emphasis on different aspects, which may be contradictory to each other. ::: In this paper, we present a method for learning the semantic similarity among images, inferring their latent aspects and embedding them into multi-spaces corresponding to their semantic aspects. ::: We consider the multi-embedding problem as an optimization function that evaluates the embedded distances with respect to the qualitative clustering queries. The key idea of our approach is to collect and embed qualitative measures that share the same aspects in bundles. To ensure similarity aspect sharing among multiple measures, image classification queries are presented to, and solved by users. The collected image clusters are then converted into bundles of tuples, which are fed into our bundle optimization algorithm that jointly infers the aspect similarity and multi-aspect embedding. Extensive experimental results show that our approach significantly outperforms state-of-the-art multi-embedding approaches on various datasets, and scales well for large multi-aspect similarity measures.


title: Using Deep Learning for Image-Based Plant Disease Detection

abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. When testing the model on a set of images collected from trusted online sources - i.e. taken under conditions different from the images used for training - the model still achieves an accuracy of 31.4%. While this accuracy is much higher than the one based on random selection (2.6%), a more diverse set of training data is needed to improve the general accuracy. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path towards smartphone-assisted crop disease diagnosis on a massive global scale.


title: Toward an Intelligent Edge: Wireless Communication Meets Machine Learning

abstract: The recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning- driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified.


title: Industry 4.0 as a Cyber-Physical System study

abstract: Advances in computation and communication are taking shape in the form of the Internet of Things, Machine-to-Machine technology, Industry 4.0, and Cyber-Physical Systems (CPS). The impact on engineering such systems is a new technical systems paradigm based on ensembles of collaborating embedded software systems. To successfully facilitate this paradigm, multiple needs can be identified along three axes: (i) online configuring an ensemble of systems, (ii) achieving a concerted function of collaborating systems, and (iii) providing the enabling infrastructure. This work focuses on the collaborative function dimension and presents a set of concrete examples of CPS challenges. The examples are illustrated based on a pick and place machine that solves a distributed version of the Towers of Hanoi puzzle. The system includes a physical environment, a wireless network, concurrent computing resources, and computational functionality such as, service arbitration, various forms of control, and processing of streaming video. The pick and place machine is of medium-size complexity. It is representative of issues occurring in industrial systems that are coming online. The entire study is provided at a computational model level, with the intent to contribute to the model-based research agenda in terms of design methods and implementation technologies necessary to make the next generation systems a reality.


title: Enhancing Entity Linking by Combining NER Models

abstract: Numerous entity linking systems are addressing the entity recognition problem by using off-the-shelf NER systems. It is, however, a difficult task to select which specific model to use for these systems, since it requires to judge the level of similarity between the datasets which have been used to train models and the dataset at hand to be processed in which we aim to properly recognize entities. In this paper, we present the newest version of ADEL, our adaptive entity recognition and linking framework, where we experiment with an hybrid approach mixing a model combination method to improve the recognition level and to increase the efficiency of the linking step by applying a filter over the types. We obtain promising results when performing a 4-fold cross validation experiment on the OKE 2016 challenge training dataset. We also demonstrate that we achieve better results that in our previous participation on the OKE 2015 test set. We finally report the results of ADEL on the OKE 2016 test set and we present an error analysis highlighting the main difficulties of this challenge.


title: Optimal graph learning with partial tags and multiple features for image and video annotation

abstract: In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometrically based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimal graph (OGL) from multi-cues (i.e., partial tags and multiple features) which can more accurately embed the relationships among the data points. We further extend our model to address out-of-sample and noisy label issues. Extensive experiments on four public datasets show the consistent superiority of OGL over state-of-the-art methods by up to 12% in terms of mean average precision.


title: Efficient Ring-LWE Encryption on 8-Bit AVR Processors

abstract: Public-key cryptography based on the “ring-variant” of the Learning with Errors (ring-LWE) problem is both efficient and believed to remain secure in a post-quantum world. In this paper, we introduce a carefully-optimized implementation of a ring-LWE encryption scheme for 8-bit AVR processors like the ATxmega128. Our research contributions include several optimizations for the Number Theoretic Transform (NTT) used for polynomial multiplication. More concretely, we describe the Move-and-Add (MA) and the Shift-Add-Multiply-Subtract-Subtract (SAMS2) technique to speed up the performance-critical multiplication and modular reduction of coefficients, respectively. We take advantage of incompletely-reduced intermediate results to minimize the total number of reduction operations and use a special coefficient-storage method to decrease the RAM footprint of NTT multiplications. In addition, we propose a byte-wise scanning strategy to improve the performance of a discrete Gaussian sampler based on the Knuth-Yao random walk algorithm. For medium-term security, our ring-LWE implementation needs 590 k, 672 k, and 276 k clock cycles for key-generation, encryption, and decryption, respectively. On the other hand, for long-term security, the execution time of key-generation, encryption, and decryption amount to 2.2 M, 2.6 M, and 686 k cycles, respectively. These results set new speed records for ring-LWE encryption on an 8-bit processor and outperform related RSA and ECC implementations by an order of magnitude.


title: Paired Associative Stimulation of Left and Right Human Motor Cortex Shapes Interhemispheric Motor Inhibition based on a Hebbian Mechanism

abstract: This study was designed to examine whether corticocortical paired associative stimulation (cc-PAS) can modulate interhemispheric inhibition (IHI) in the human brain. Twelve healthy right-handed volunteers received 90 paired transcranial stimuli to the right and left primary motor hand area (M1HAND) at an interstimulus interval (ISI) of 8 ms. Left-to-right cc-PAS (first pulse given to left M1HAND) attenuated left-to-right IHI for one hour after cc-PAS. Left-to-right cc-PAS also increased corticospinal excitability in the conditioned right M1HAND. These effects were not seen in an asymptomatic individual with callosal agenesis. Additional experiments showed no changes in left-to-right IHI or corticospinal excitability when leftto-right cc-PAS was given at an ISI of 1 ms or at multiple ISIs in random order. At the behavioral level, left-to-right cc-PAS speeded responses with the left but not right index finger during a simple reaction time task. Right-to-left cc-PAS (first pulse given to right M1HAND) reduced right-to-left IHI without increasing corticospinal excitability in left M1HAND. These results provide a proof of principle that cc-PAS can induce associative plasticity in connections between the targeted cortical areas. The efficacy of cc-PAS to induce lasting changes in excitability depends on the exact timing of the stimulus pairs suggesting an underlying Hebbian mechanism.


title: Clustering with t-SNE, provably

abstract: t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and visualization method proposed by van der Maaten & Hinton in 2008, has rapidly become a standard tool in a number of natural sciences. Despite its overwhelming success, there is a distinct lack of mathematical foundations and the inner workings of the algorithm are not well understood. The purpose of this paper is to prove that t-SNE is able to recover well-separated clusters; more precisely, we prove that t-SNE in the `early exaggeration' phase, an optimization technique proposed by van der Maaten & Hinton (2008) and van der Maaten (2014), can be rigorously analyzed. As a byproduct, the proof suggests novel ways for setting the exaggeration parameter $\alpha$ and step size $h$. Numerical examples illustrate the effectiveness of these rules: in particular, the quality of embedding of topological structures (e.g. the swiss roll) improves. We also discuss a connection to spectral clustering methods.


title: Building a Hybrid SAT Solver via Conflict-Driven, Look-Ahead and XOR Reasoning Techniques

abstract: The paper develops a new hybrid SAT solver, called MoRsat. The framework of the solver is based on a look-ahead technique, and its core is a conflict-driven search. A look-ahead technique is used to split the original problem to sub-problems, each of them is either solved or aborted by a conflict-driven DPLL. Aborted sub-problems are solved recursively. We present new properties of XOR clauses, which are used to incorporate XOR reasoning into our conflict-driven DPLL. Compared with the latest versions of Rsat and March, the Gold Medal winners in the industrial and handmade SAT category of the SAT 2007 competition, MoRsat achieves remarkable improvements. Moreover, MoRsat can solve some industrial instances that were not solved in the SAT 2007 competition.


title: Synchronous neural oscillations and cognitive processes

abstract: The central problem for cognitive neuroscience is to describe how cognitive processes arise from brain processes. This review summarizes the recent evidence that synchronous neural oscillations reveal much about the origin and nature of cognitive processes such as memory, attention and consciousness. Memory processes are most closely related to theta and gamma rhythms, whereas attention seems closely associated with alpha and gamma rhythms. Conscious awareness may arise from synchronous neural oscillations occurring globally throughout the brain rather than from the locally synchronous oscillations that occur when a sensory area encodes a stimulus. These associations between the dynamics of the brain and cognitive processes indicate progress towards a unified theory of brain and cognition.


title: Phased array ghost elimination

abstract: Parallel imaging may be applied to cancel ghosts caused by a variety of distortion mechanisms, including distortions such as off-resonance or local flow, which are space variant. Phased array combining coefficients may be calculated that null ghost artifacts at known locations based on a constrained optimization, which optimizes SNR subject to the nulling constraint. The resultant phased array ghost elimination (PAGE) technique is similar to the method known as sensitivity encoding (SENSE) used for accelerated imaging; however, in this formulation is applied to full field-of-view (FOV) images. The phased array method for ghost elimination may result in greater flexibility in designing acquisition strategies. For example, in multi-shot EPI applications ghosts are typically mitigated by the use of an interleaved phase encode acquisition order. An alternative strategy is to use a sequential, non-interleaved phase encode order and cancel the resultant ghosts using PAGE parallel imaging. Cancellation of ghosts by means of phased array processing makes sequential, non-interleaved phase encode acquisition order practical, and permits a reduction in repetition time, TR, by eliminating the need for echo-shifting. Sequential, non-interleaved phase encode order has benefits of reduced distortion due to off-resonance, in-plane flow and EPI delay misalignment. Furthermore, the use of EPI with PAGE has inherent fat-water separation and has been used to provide off-resonance correction using a technique referred to as lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA), and may further generalized using the multi-point Dixon method. Other applications of PAGE include cancelling ghosts which arise due to amplitude or phase variation during the approach to steady state. Parallel imaging requires estimates of the complex coil sensitivities. In vivo estimates may be derived by temporally varying the phase encode ordering to obtain a full k-space dataset in a scheme similar to the autocalibrating TSENSE method. This scheme is a generalization of the UNFOLD method used for removing aliasing in undersampled acquisitions. The more general scheme may be used to modulate each EPI ghost image to a separate temporal frequency as described in this paper. Copyright © 2006 John Wiley & Sons, Ltd.


title: Retinal prosthesis for the blind.

abstract: Most of current concepts for a visual prosthesis are based on neuronal electrical stimulation at different locations along the visual pathways within the central nervous system. The different designs of visual prostheses are named according to their locations (i.e., cortical, optic nerve, subretinal, and epiretinal). Visual loss caused by outer retinal degeneration in diseases such as retinitis pigmentosa or age-related macular degeneration can be reversed by electrical stimulation of the retina or the optic nerve (retinal or optic nerve prostheses, respectively). On the other hand, visual loss caused by inner or whole thickness retinal diseases, eye loss, optic nerve diseases (tumors, ischemia, inflammatory processes etc.), or diseases of the central nervous system (not including diseases of the primary and secondary visual cortices) can be reversed by a cortical visual prosthesis. The intent of this article is to provide an overview of current and future concepts of retinal and optic nerve prostheses. This article will begin with general considerations that are related to all or most of visual prostheses and then concentrate on the retinal and optic nerve designs. The authors believe that the field has grown beyond the scope of a single article so cortical prostheses will be described only because of their direct effect on the concept and technical development of the other prostheses, and this will be done in a more general and historic perspective.


title: Practical Lessons From the Deployment and Management of a Smart City Internet-of-Things Infrastructure: The SmartSantander Testbed Case

abstract: The smart cities vision is inexorably turning into a reality. Among the different approaches used to realize more intelligent and sustainable environments, a common denominator is the role that information and communication technologies will play. Moreover, if there is one of these technologies that emerges among the rest, it is the Internet-of-Things (IoT). The ability to ubiquitously embed sensing and actuating capabilities that this paradigm enables is at the forefront of the technologies driving the urban environments transformation. However, there are very little practical experiences of the IoT infrastructure deployment at a large scale. This paper presents practical solutions to the main challenges faced during the deployment and management of a city-scale IoT infrastructure, which encompasses thousands of sensors and other information sources. The experience we have gained during the deployment and operation of the IoT-based smart city infrastructure carried out at Santander (Spain) has led to a number of practical lessons that are summarized in this paper. Moreover, the challenges and problems examples, excerpted from our own real-life experience, are described as motivators for the adopted solutions.


Comparison Of Frankel Regulator And Cervera Appliance In Functional Treatment Of Class II Malocclusions: A Systematic Review


title: Fast maximum margin matrix factorization for collaborative prediction

abstract: Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approximations and standard factor models. MMMF can be formulated as a semi-definite programming (SDP) and learned using standard SDP solvers. However, current SDP solvers can only handle MMMF problems on matrices of dimensionality up to a few hundred. Here, we investigate a direct gradient-based optimization method for MMMF and demonstrate it on large collaborative prediction problems. We compare against results obtained by Marlin (2004) and find that MMMF substantially outperforms all nine methods he tested.


CS 512 Final : Tweet Sentiment Analysis Using Syntactic Tweets and Ensemble Neural Nets


Extending the Definitions of Antenna Gain and Radiation Pattern Into the Time Domain


title: The meaning of beauty: implicit and explicit self-esteem and attractiveness beliefs in body dysmorphic disorder.

abstract: Abstract Body dysmorphic disorder (BDD) is characterized by imagined or slight defects in one's appearance. We evaluated implicit and explicit biases among individuals diagnosed with BDD ( n = 21), individuals with subclinical BDD symptoms ( n = 21), and healthy control participants ( n = 21). Specifically, we used the Implicit Association Test [IAT; Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: the implicit association test. Journal of Personality and Social Psychology , 74 , 1464–1480] to examine automatic associations related to self-esteem, associations between attractive and important, and a physical attractiveness stereotype (associations between attractive and competent). BDD participants had significantly lower implicit self-esteem, relative to control participants, and the subclinical BDD participants were intermediate between these groups. Although no group differences were observed on the implicit Attractive Important IAT; as predicted, BDD participants had significantly stronger implicit associations between attractive and competent than the other groups, in line with a common stereotype about physical attractiveness. Both the Attractive Competent IAT and Self-Esteem IAT were significant predictors of BDD symptom severity, and distress and avoidance during a mirror exposure task. Findings are discussed in light of cognitive-behavioral models of BDD.


title: On wave equations of the $p$-Laplacian type with supercritical nonlinearities

abstract: Abstract This article focuses on a quasilinear wave equation of p -Laplacian type: u t t − Δ p u − Δ u t = f ( u ) in a bounded domain Ω ⊂ R 3 with a sufficiently smooth boundary Γ = ∂ Ω subject to a generalized Robin boundary condition featuring boundary damping and a nonlinear source term. The operator Δ p , 2 p 3 , denotes the classical p -Laplacian. The interior and boundary terms f ( u ) , h ( u ) are sources that are allowed to have a supercritical exponent, in the sense that their associated Nemytskii operators are not locally Lipschitz from W 1 , p ( Ω ) into L 2 ( Ω ) or L 2 ( Γ ) . Under suitable assumptions on the parameters we provide a rigorous proof of existence of a local weak solution which can be extended globally in time, provided the damping terms dominate the corresponding sources in an appropriate sense. Moreover, a blow-up result is proved for solutions with negative initial total energy.


title: Addendum to"Single photon logic gates using minimum resources"

abstract: The authors call attention to a previous work [Qing Lin and Bing He, Phys. Rev. A 80, 042310 (2009)] on the realization of multi-qubit logic gates with controlled-path and merging gate. We supplement the work by showing how to efficiently build realistic quantum circuits in this approach and giving the guiding rules for the task.


title: Flying ad-hoc networks (FANETs): A review of communication architectures, and routing protocols

abstract: With recent technological progress in the field of electronics, sensors and communication systems, the production of small UAVs (Unmanned Air Vehicles) became possible, which can be used for several military, commercial and civilian applications. However, the capability of a single and small UAV is inadequate. Multiple-UAVs can make a system that is beyond the limitations of a single small UAV. A Flying Ad hoc Networks (FANETs) is such kind of network that consists of a group of small UAVs connected in ad-hoc manner, which are integrated into a team to achieve high level goals. Mobility, lack of central control, self-organizing and ad-hoc nature between the UAVs are the main features of FANETs, which could expand the connectivity and extend the communication range at infrastructure-less area. On one hand, in case of catastrophic situations when ordinary communication infrastructure is not available, FANETs can be used to provide a rapidly deployable, flexible, self-configurable and relatively small operating expenses network; the other hand connecting multiple UAVs in ad-hoc network is a big challenge. This level of coordination requires an appropriate communication architecture and routing protocols that can be set up on highly dynamic flying nodes in order to establish a reliable and robust communication. The main contribution of this paper include the introduction of suitable communication architecture, and an overview of different routing protocols for FANETs. The open research issues of existing routing protocols are also investigated in this paper.


title: Droop control in LV-grids

abstract: Remote electrification with island supply systems, the increasing acceptance of the microgrids concept and the penetration of the interconnected grid with DER and RES require the application of inverters and the development of new control algorithms. One promising approach is the implementation of conventional f/U-droops into the respective inverters, thus down scaling the conventional grid control concept to the low voltage grid. Despite contradicting line parameters, the applicability of this proceeding is outlined and the boundary conditions are derived


title: BackFi: High Throughput WiFi Backscatter

abstract: We present BackFi, a novel communication system that enables high throughput, long range communication between very low power backscatter devices and WiFi APs using ambient WiFi transmissions as the excitation signal. Specifically, we show that it is possible to design devices and WiFi APs such that the WiFi AP in the process of transmitting data to normal WiFi clients can decode backscatter signals which the devices generate by modulating information on to the ambient WiFi transmission. We show via prototypes and experiments that it is possible to achieve communication rates of up to 5 Mbps at a range of 1 m and 1 Mbps at a range of 5 meters. Such performance is an order to three orders of magnitude better than the best known prior WiFi backscatter system [27,25]. BackFi design is energy efficient, as it relies on backscattering alone and needs insignificant power, hence the energy consumed per bit is small.


title: A pipeline architecture for computing the Euler number of a binary image

abstract: Euler number of a binary image is a fundamental topological feature that remains invariant under translation, rotation, scaling, and rubber-sheet transformation of the image. In this work, a run-based method for computing Euler number is formulated and a new hardware implementation is described. Analysis of time complexity and performance measure is provided to demonstrate the efficiency of the method. The sequential version of the proposed algorithm requires significantly fewer number of pixel accesses compared to the existing methods and tools based on bit-quad counting or quad-tree, both for the worst case and the average case. A pipelined architecture is designed with a single adder tree to implement the algorithm on-chip by exploiting its inherent parallelism. The architecture uses O(N) 2-input gates and requires O(NlogN) time to compute the Euler number of an N × N image. The same hardware, with minor modification, can be used to handle arbitrarily large pixel matrices. A standard cell based VLSI implementation of the architecture is also reported. As Euler number is a widely used parameter, the proposed design can be readily used to save computation time in many image processing applications.


title: Precise position and trajectory control of pneumatic soft-actuators for assistance robots and motion therapy devices

abstract: The goal of this work is the development and performance analysis of control algorithms for new soft fluidic actuators with rotary elastic chambers (REC-actuators). Due to their inherent compliancy these actuators fulfill the requisites for building intrinsically safe mechanisms as assistance robots and motion therapy devices, working in direct physical contact with humans. Besides the difficulties common for control design of pneumatic systems, these actuators itself posses several nonlinear characteristics causing specific problems in their modeling and control. In this work the decentralized joint control scheme is implemented, where the position controller has a cascade structure with a non-linear model based pressure control in the inner loop. Two different position control approaches, which require minimal information on the dynamics of the actuator mechanical subsystem, were investigated and tested: sliding mode control with time delay estimation as well as a fuzzy control with parameter optimization based on genetic algorithms.


title: Metrology and visualization of potholes using the microsoft kinect sensor

abstract: Pavement distress and wear detection is of prime importance in transportation engineering. Due to degradation, potholes and different types of cracks are formed and they have to be detected and repaired in due course. Estimating the amount of filler material that is needed to fill a pothole is of great interest to prevent any shortage or excess, thereby wastage, of filler material that usually has to be transported from a different location. Metrological and visualization properties of a pothole play an important role in this regard. Using a low-cost Kinect sensor, the pavement depth images are collected from concrete and asphalt roads. Meshes are generated for better visualization of potholes. Area of pothole is analyzed with respect to depth. The approximate volume of pothole is calculated using trapezoidal rule on area-depth curves through pavement image analysis. In addition pothole area, length, and width are estimated. The paper also proposes a methodology to characterize potholes.


title: Teaching Computational Thinking in K-6: The CSER Digital Technologies MOOC

abstract: In recent decades, ICT curriculum in K-10 has typically focussed on ICT as a tool, with the development of digital literacy being the key requirement. Areas such as computer science (CS) or computational thinking (CT) were typically isolated into senior secondary programs, with a focus on programming and algorithm development, when they were considered at all. New curricula introduced in England, and currently awaiting minister endorsement within Australia, have identified the need to educate for both digital literacy and CS, and the need to promote both from the commencement of schooling. This has presented significant challenges for teachers within this space, as they generally do not have the disciplinary knowledge to teach new computing curriculum and pedagogy in the early years is currently underdeveloped. In this paper, we introduce the CSER Digital Technologies MOOC, assisting teachers in the development of the fundamental knowledge of CT and the Australian Digital Technologies curriculum component. We describe our course structure, and key mechanisms for building a learning community within a MOOC context. We identify key challenges that teachers have identified in mastering this new curriculum, highlighting areas of future research in the teaching and learning of CT in K-6.


title: An Adaptive Spectral Element Method for Two-Fluid Flows

abstract: A new mesh adaptation technique for two dimensional deforming domains has been combined with an Arbitrary-Lagrangian-Eulerian, spectral/hp finite element solver. This combination provides a powerful simulation tool for two-fluid flows; With the spectral/hp finite element method, high accuracy solutions can be obtained efficiently, but by itself this technique is limited to problems with moderate interface deformation. By combining it with a mesh adaptation strategy for deforming domains, large deformation problems can be simulated. In addition, solution-based mesh refinement can be performed which is critical for resolving multi-scale two-fluid problems. The capabilities of these techniques together are demonstrated for some preliminary examples.Copyright © 2002 by ASME


title: Security in automotive bus systems

abstract: This work presents a study of current and future bus systems with respect to their security against various malicious attacks. After a brief description of the most well-known and established vehicular communication systems, we present feasible attacks and potential exposures for these automotive networks. We also provide an approach for secured automotive communication based on modern cryptographic mechanisms that provide secrecy, manipulation prevention and authentication to solve most of the vehicular bus security issues.


When Does More Money Work? Examining the Role of Perceived Fairness in Pay on the Performance Quality of Crowdworkers


title: Joint Optimization of Service Function Chaining and Resource Allocation in Network Function Virtualization

abstract: Network function virtualization (NFV) has already been a new paradigm for network architectures. By migrating NFs from dedicated hardware to virtualization platform, NFV can effectively improve the flexibility to deploy and manage service function chains (SFCs). However, resource allocation for requested SFC in NFV-based infrastructures is not trivial as it mainly consists of three phases: virtual network functions (VNFs) chain composition, VNFs forwarding graph embedding, and VNFs scheduling. The decision of these three phases can be mutually dependent, which also makes it a tough task. Therefore, a coordinated approach is studied in this paper to jointly optimize NFV resource allocation in these three phases. We apply a general cost model to consider both network costs and service performance. The coordinate NFV-RA is formulated as a mixed-integer linear programming, and a heuristic-based algorithm (JoraNFV) is proposed to get the near optimal solution. To make the coordinated NFV-RA more tractable, JoraNFV is divided into two sub-algorithms, one-hop optimal traffic scheduling and a multi-path greedy algorithm for VNF chain composition and VNF forwarding graph embedding. Last, extensive simulations are performed to evaluate the performance of JoraNFV, and results have shown that JoraNFV can get a solution within 1.25 times of the optimal solution with reasonable execution time, which indicates that JoraNFV can be used for online NFV planning.


title: Design of Mine Ventilators Monitoring System Based on Wireless Sensor Network

abstract: A monitoring system for a mine ventilator is designed based on ZigBee wireless sensor network technology in the paper. The system consists of a sink node, sensor nodes, industrial personal computer and several sensors. Sensor nodes communicate with the sink node through the ZigBee wireless sensor network. The sink node connects with the configuration software on the pc via serial port. The system can collect or calculate vibration, temperature, negative pressure, air volume and other information of the mine ventilator. Meanwhile the system accurately monitors operating condition of the ventilator through these parameters. Especially it provides the most original information for potential faults of the ventilator. Therefore, there is no doubt that it improves the efficiency of fault diagnosis.


title: Multiport Pixel Rectenna for Ambient RF Energy Harvesting

abstract: We describe the design of a multiport pixel rectenna for ambient radio-frequency (RF) energy harvesting consisting of an optimized triple-port pixel antenna and a triple-port rectifier with dc combining. The advantages of the multiport pixel approach include enhanced harvested RF power for a given area as well as a reduction in the antenna matching requirements. We formulate the design of the triple-port pixel antenna as a binary optimization problem with an objective function related to harvested RF power in the GSM-1800 band for specified power angular spectrums without the need for antenna matching components. The optimization of the triple-port pixel antenna is obtained by using successive exhaustive Boolean optimization. The design for the triple-port rectifier with dc combining is also provided and a prototype is demonstrated. The rectenna measurement demonstrates that the proposed triple-port pixel antenna has dc output power over double that of single-port-based antennas of similar size. The overall RF-to-dc efficiency of the multiport pixel rectenna is also evaluated and shown to be 19% when the total input RF power is −20 dBm. The effects of nonuniformity in the input RF power across antenna ports are also investigated.