Broadband Substrate Integrated Waveguide Cavity-Backed Bow-Tie Slot Antenna. A novel design technique for broadband substrate integrated waveguide cavity-backed slot antenna is demonstrated in this letter. Instead of using a conventional narrow rectangular slot, a bow-tie-shaped slot is implemented to get broader bandwidth performance. The modification of the slot shape helps to induce strong loading effect in the cavity and generates two closely spaced hybrid modes that help to get a broadband response. The slot antenna incorporates thin cavity backing (height <;0.03λ0 ) in a single substrate and thus retains low-profile planar configuration while showing unidirectional radiation characteristics with moderate gain. A fabricated prototype is also presented that shows a bandwidth of 1.03 GHz (9.4%), a gain of 3.7 dBi over the bandwidth, 15 dB front-to-back ratio, and cross-polarization level below -18 dB.

Question: What is a bow-tie-shaped slot?


A refined PWM scheme for voltage and current source converters. A pulse-width-modulation (PWM) scheme that uses the converter switching frequency to minimize unwanted load current harmonics is described. This results in the reduction of the number of switch communications per cycle. The method is suitable for high-performance variable-speed AC-motor drives that require high-output switching frequencies for near-silent operation. It is also applicable, without change, to voltage or current-source inverters and to two and four-quadrant three-phase PWM rectifiers. Key predicted results have been verified experimentally on a 5 kVA inverter breadboard.<<ETX>>

Question: What is ETX?


Multilevel resilience analysis of transportation and communication networks. For many years the research community has attempted to model the Internet in order to better understand its behaviour and improve its performance. Since much of the structural complexity of the Inter*Work performed while Egemen K. Çetinkaya, Andrew M. Peck, and Justin P. Rohrer were at the University of Kansas. Egemen K. Çetinkaya Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA cetinkayae@mst.edu, +1 573 341 6887 Mohammed J.F. Alenazi Department of Electrical Engineering and Computer Science, Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, USA malenazi@ittc.ku.edu Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia Andrew M. Peck apeck@ittc.ku.edu; andrewpeck11@gmail.com Justin P. Rohrer rohrej@ittc.ku.edu Department of Computer Science, Naval Postgraduate School, Monterey, CA 93943–5285, jprohrer@nps.edu James P.G. Sterbenz Department of Electrical Engineering and Computer Science, Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, USA https://www.ittc.ku.edu/resilinets jpgs@ittc.ku.edu, +1 508 944 3067 School of Computing and Communications, InfoLab21 Lancaster University, Lancaster, UK, jpgs@comp.lancs.ac.uk Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong jpgs@comp.polyu.edu.hk net is due to its multilevel operation, the Internet's multilevel nature is an important and non-trivial feature that researchers must consider when developing appropriate models. In this paper, we compare the normalised Laplacian spectra of physicaland logical-level topologies of four commercial ISPs and two research networks against the US freeway topology, and show analytically that physical level communication networks are struc

Question: What is the difference between the normalised Laplacian spectra of physical and logical level topologies of four commercial ISPs and two research networks against the US freeway topology?


Anomaly Detection and Attribution Using Bayesian Networks. We present a novel approach to anomaly detection in Bayesian networks, enabling both the detection and explanation of anomalous cases in a dataset. By exploiting the structure of a Bayesian network, our algorithm is able to efficiently search for local maxima of data conflict between closely related variables. Benchmark tests using data simulated from complex Bayesian networks show that our approach provides a significant improvement over techniques that search for anomalies using the entire network, rather than its subsets. We conclude with demonstrations of the unique explanatory power of our approach in determining the observation(s) responsible for an anomaly. APPROVED FOR PUBLIC RELEASE

Question: What is the difference between a Bayesian network and a Bayesian network?


An Injectable 64 nW ECG Mixed-Signal SoC in 65 nm for Arrhythmia Monitoring. A syringe-implantable electrocardiography (ECG) monitoring system is proposed. The noise optimization and circuit techniques in the analog front-end (AFE) enable 31 nA current consumption while a minimum energy computation approach in the digital back-end reduces digital energy consumption by 40%. The proposed SoC is fabricated in 65 nm CMOS and consumes 64 nW while successfully detecting atrial fibrillation arrhythmia and storing the irregular waveform in memory in experiments using an ECG simulator, a live sheep, and an isolated sheep heart.

Question: What is a syringe-implantable ECG monitoring system?


Statistical Methods in Psychology Journals Guidelines and Explanations. n the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was "to elucidate some of the controversial issues surrounding applications of statistics including significance testing and its alternatives; alternative underlying models and data transformation; and newer methods made possible by powerful computers" (BSA, personal communication, February 28, 1996). Robert Rosenthal, Robert Abelson, and Jacob Cohen (cochairs) met initially and agreed on the desirability of having several types of specialists on the task force: statisticians, teachers of statistics, journal editors, authors of statistics books, computer experts, and wise elders. Nine individuals were subsequently invited to join and all agreed. These were Leona Aiken, Mark Appelbaum, Gwyneth Boodoo, David A. Kenny, Helena Kraemer, Donald Rubin, Bruce Thompson, Howard Wainer, and Leland Wilkinson. In addition, Lee Cronbach, Paul Meehl, Frederick Mosteller and John Tukey served as Senior Advisors to the Task Force and commented on written materials. The TFSI met twice in two years and corresponded throughout that period. After the first meeting, the task force circulated a preliminary report indicating its intention to examine issues beyond null hypothesis significance testing. The task force invited comments and used this feedback in the deliberations during its second meeting. After the second meeting, the task force recommended several possibilities for further action, chief of which would be to revise the statistical sections of the American Psychological Association Publication Manual (APA, 1994). After extensive discussion, the BSA recommended that "before the TFS

Question: What is the difference between significance testing and hypothesis testing?


Mining Efficient Taxi Operation Strategies From Large Scale Geo-Location Data. Taxi drivers always look for strategies to locate passengers quickly and therefore increase their profit margin. In reality, the passenger seeking strategies are mostly empirical and substantially vary among taxi drivers. From the history taxi data, the top performing taxi drivers can earn 25% more than the ones with mediocre seeking strategy in the same period of time. A better strategy not only helps taxi drivers earn more with less effort, but also reduce fuel consumption and carbon emissions. It is interesting to examine the influential factors in passenger seeking strategies and find algorithms to guide taxi drivers to passenger hotspots with the right timing. With the abundant availability of history taxicab traces, the existing methods of doing taxi business have been radically changed. This paper focuses on the problem of mining efficient operation strategies from a large-scale history taxi traces collected over one year. Our approach presents generic insights into the dynamics of taxicab services with the objective of maximizing the profit margins for the concerned parties. We propose important metrics, such as trip frequency, hot spots, and taxi mileage, and provide valuable insights toward more efficient operation strategies. We analyze these metrics using techniques, such as Newton's polynomial interpolation and Gamma distribution, to understand their dynamics. Our strategies use the real taxicab traces from the city of Changsha (P.R.China), may predict the taxi rides at different times by 90.68% per day, and increase the taxi drivers income levels up to 19.38% by controlling appropriate mileage per trip and following the route across more urban hot spots.

Question: How to predict taxi rides?


Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

Question: What is the difference between deep 3D convolutional encoder networks with shortcut connections and deep 3D convolutional encoder networks with shortcut connections applied to multiple sclerosis lesion segmentation?


Deep Transfer in Reinforcement Learning by Language Grounding. In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized representation to effectively utilize entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments.

Question: What is a model-based reinforcement learning?


Supervised tensor learning. Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem.

Question: What is the tensor representation?


Techniques for the Automated Analysis of Musical Audio. This thesis presents work on automated analysis techniques for musical audio. A complete analysis of the content within an audio waveform is termed 'transcription' and can be used for intelligent, informed processing of the signal in a variety of applications. However, full transcription is beyond the ability of current methods and hence this thesis concerns subtasks of whole problem. A major part of musical analysis is the extraction of signal information from a time-frequency representation, often the short time Fourier transform or spectrogram. The 'reassignment' method is a technique for improving the clarity of these representations. It is comprehensively reviewed and its relationship to a number of classical instantaneous frequency measures is explicitly stated. Performance of reassignment as a sinusoidal frequency estimator is then assessed quantitatively for the first time. This is achieved in the framework of the Cramér-Rao bound. Reassignment is shown to be inferior to some other estimators for simple synthetic signals but achieves comparable results for real musical examples. Several extensions and uses of the reassignment method are then described. These include the derivation of reassignment measures extracted from the amplitude of the spectrogram, rather than the traditional phase-based method and the use of reassignment measures for signal classification. The second major area of musical analysis investigated in this thesis is beat tracking, where the aim is to extract both the tempo implied in the music and also its phase which is analagous to beat location. Three models for the beat in general music examples are described. These are cast in a state-space formulation within the Bayesian paradigm, which allows the use of Monte Carlo methods for inference of the model parameters. The first two models use pre-extracted note onsets and model tempo as either a linear Gaussian process or as Brownian m

Question: What is the difference between a musical note and a musical beat?


Implant Removal after Percutaneous Short Segment Fixation for Thoracolumbar Burst Fracture : Does It Preserve Motion?. OBJECTIVE The purpose of this study was to evaluate the efficacy of implant removal of percutaneous short segment fixation after vertebral fracture consolidation in terms of motion preservation.   METHODS Between May 2007 and January 2011, 44 patients underwent percutaneous short segment screw fixation due to a thoracolumbar burst fracture. Sixteen of these patients, who underwent implant removal 12 months after screw fixation, were enrolled in this study. Motor power was intact in all patients, despite significant vertebral height loss and canal compromise. The patients were divided into two groups by degree of osteoporosis : Group A (n=8), the non-osteoporotic group, and Group B (n=8), the osteoporotic group. Imaging and clinical findings including vertebral height loss, kyphotic angle, range of motion (ROM), and complications were analyzed.   RESULTS Significant pain relief was achieved in both groups at final follow-up versus preoperative values. In terms of vertebral height loss, both groups showed significant improvement at 12 months after screw fixation and restored vertebral height was maintained to final follow-up in spite of some correction loss. ROM (measured using Cobb's method) in flexion and extension in Group A was 10.5° (19.5/9.0°) at last follow-up, and in Group B was 10.2° (18.8/8.6°) at last follow-up. Both groups showed marked improvement in ROM as compared with the screw fixation state, which was considered motionless.   CONCLUSION Removal of percutaneous implants after vertebral fracture consolidation can be an effective treatment to preserve motion regardless of osteoporosis for thoracolumbar burst fractures.

Question: Does implant removal after percutaneous short segment fixation preserve motion?


Genetic Ablation of Orexin Neurons in Mice Results in Narcolepsy, Hypophagia, and Obesity. Orexins (hypocretins) are a pair of neuropeptides implicated in energy homeostasis and arousal. Recent reports suggest that loss of orexin-containing neurons occurs in human patients with narcolepsy. We generated transgenic mice in which orexin-containing neurons are ablated by orexinergic-specific expression of a truncated Machado-Joseph disease gene product (ataxin-3) with an expanded polyglutamine stretch. These mice showed a phenotype strikingly similar to human narcolepsy, including behavioral arrests, premature entry into rapid eye movement (REM) sleep, poorly consolidated sleep patterns, and a late-onset obesity, despite eating less than nontransgenic littermates. These results provide evidence that orexin-containing neurons play important roles in regulating vigilance states and energy homeostasis. Orexin/ataxin-3 mice provide a valuable model for studying the pathophysiology and treatment of narcolepsy.

Question: What is the role of orexin in narcolepsy?


A cross-national profile of bullying and victimization among adolescents in 40 countries. (1) To compare the prevalence of bullying and victimization among boys and girls and by age in 40 countries. (2) In 6 countries, to compare rates of direct physical, direct verbal, and indirect bullying by gender, age, and country. Cross-sectional self-report surveys including items on bullying and being bullied were obtained from nationally representative samples of 11, 13 and 15 year old school children in 40 countries, N = 202,056. Six countries (N = 29,127 students) included questions about specific types of bullying (e. g., direct physical, direct verbal, indirect). Exposure to bullying varied across countries, with estimates ranging from 8.6% to 45.2% among boys, and from 4.8% to 35.8% among girls. Adolescents in Baltic countries reported higher rates of bullying and victimization, whereas northern European countries reported the lowest prevalence. Boys reported higher rates of bullying in all countries. Rates of victimization were higher for girls in 29 of 40 countries. Rates of victimization decreased by age in 30 of 40 (boys) and 25 of 39 (girls) countries. There are lessons to be learned from the current research conducted in countries where the prevalence is low that could be adapted for use in countries with higher prevalence.

Question: What is the prevalence of bullying and victimization among adolescents in 40 countries?


Consumers ' Trust in Electronic Commerce Transactions : The Role of Perceived Privacy and Perceived Security 1 Introduction. Acknowledgement I am greatly indebted to Omar El Sawy and Ann Majchrzak for their guidance and suggestion, and I would like to thank Ricky Lim and Raymond for their help with the data analysis. Abstract Consumers' trust in their online transactions is vital for the sustained progress and development of electronic commerce. Our paper proposes that in addition to known factors of trust such a vendor's reputation, consumers' perception of privacy and security influence their trust in online transactions. Our research shows that consumers exhibit variability in their perceptions of privacy, security and trust between online and offline transactions even if it is conducted with the same store. We build upon this finding to develop and validate measures of consumers' perceived privacy and perceived security of their online transactions which are then theorized to influence their trust in EC transactions. We propose that the perceptions of privacy and security are factors that affect the consumers' trust in the institutional governance mechanisms underlying the Internet. We conduct two distinct empirical studies and through successive refinement and analysis using the Partial Least Squares technique, we test our hypothesized relationships while verifying the excellent measurement properties associated with our instrument. Our study finds that the consumers' perceived privacy and perceived security are indeed distinct constructs but the effect of perceived privacy on trust in EC transactions is strongly mediated by perceived security. A major implication of our findings is that while the much studied determinants of trust such as reputation of the transacting firm should not be neglected, vendors should also engage in efforts to positively influence consumer perceptions of privacy and security. We discuss the significance of this observation in the co

Question: What is the difference between perceived privacy and perceived security?


Mapping Large Spatial Flow Data with Hierarchical Clustering. It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.

Question: How to map large spatial flow data?


Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture. In this paper we present a perception system for agriculture robotics that enables an unmanned ground vehicle (UGV) equipped with a multi spectral camera to automatically perform the crop/weed detection and classification tasks in real-time. Our approach exploits a pipeline that includes two different convolutional neural networks (CNNs) applied to the input RGB+near infra-red (NIR) images. A lightweight CNN is used to perform a fast and robust, pixelwise, binary image segmentation, in order to extract the pixels that represent projections of 3D points that belong to green vegetation. A deeper CNN is then used to classify the extracted pixels between the crop and weed classes. A further important contribution of this work is a novel unsupervised dataset summarization algorithm that automatically selects from a large dataset the most informative subsets that better describe the original one. This enables to streamline and speed-up the manual dataset labeling process, otherwise extremely time consuming, while preserving good classification performances. Experiments performed on different datasets taken from a real farm robot confirm the effectiveness of our approach.

Question: What is a perception system for agriculture robotics?


Mathematical modeling of photovoltaic module with Simulink. This paper presents a unique step-by-step procedure for the simulation of photovoltaic modules with Matlab/ Simulink. One-diode equivalent circuit is employed in order to investigate I-V and P-V characteristics of a typical 36 W solar module. The proposed model is designed with a user-friendly icons and a dialog box like Simulink block libraries.

Question: How to simulate a photovoltaic module with Matlab/Simulink?


Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering. Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient, cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion. Therefore, two users have equal impact on each other in recommending new items. In this paper, we introduce new weighting schemes that allow us to consider new features in finding similarities between users. These weighting schemes, first, transform symmetric similarity to asymmetric similarity by considering the number of ratings given by users on non-common items. Second, they take into account the habit effects of users are regarded on rating items by measuring the proximity of the number of repetitions for each rate on common rated items. Experiments on two datasets were implemented and compared to other similarity measures. The results show that adding weighted schemes to traditional similarity measures These authors contributed equally to this work as the first author. P. Pirasteh · D. Hwang Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea P. Pirasteh e-mail: parivash63@gmail.com D. Hwang e-mail: dosamhwang@gmail.com J. E. Jung ( ) Department of Computer Engineering, Chung-Ang University, Seoul, Republic of Korea e-mail: ontology.society@gmail.com significantly improve the results obtained from traditional similarity measures.

Question: What is the difference between the Pearson correlation coefficient and the cosine similarity?


Visualizing Time-Dependent Data Using Dynamic t-SNE. Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-dimensional real vectors), and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t-SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t-SNE, an adaptation of t-SNE that introduces a controllable trade-off between temporal coherence and projection reliability. Our evaluation in two time-dependent datasets shows that dynamic t-SNE eliminates unnecessary temporal variability and encourages smooth changes between projections.

Question: How to visualize time-dependent data?


Projection Based Weight Normalization for Deep Neural Networks. Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.

Question: How to optimize deep neural networks?


Exploring Various Knowledge in Relation Extraction. Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.

Question: What is the best way to incorporate diverse knowledge in relation extraction?


Mobile Robot Control on a Reference Path. In this paper a control design of a nonholonomic mobile robot with a differential drive is presented. On the basis of robot kinematics equations a robot control is designed where the robot is controlled to follow the arbitrary path reference with a predefined velocity profile. The designed control algorithm proved stable and robust to the errors in robot initial positions, to input and output noises and to other disturbances. The obtained control law is demonstrated on a simple trajectory example, however, for a more general applicability a time-optimal motion planning algorithm considering acceleration constraints is presented as well

Question: How to control a robot to follow a path?


Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection. Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay MWSG filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time-frequency T-F plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score $10.24\%$ relative compared to the best of the six baseline schemes considered in this work.

Question: How to detect bird sounds?


Design and Implementation of a Pure Sine Wave Single Phase Inverter for Photovoltaic Applications. This paper aims at developing the control circuit for a single phase inverter which produces a pure sine wave with an output voltage that has the same magnitude and frequency as a grid voltage. A microcontroller, based on an advanced technology to generate a sine wave with fewer harmonics, less cost and a simpler design. The technique used is the sinusoidal pulse width modulation signal (SPWM) which is generated by microcontroller. The designed inverter is tested on various AC loads and is essentially focused upon low power electronic applications such as a lamp, a fan and chargers etc. The proposed model of the inverter can improve the output wave forms of the inverter and the dead time control reduced to 63μs. The finished design is simulated in Proteus and PSIM software to ensure output results which is verified practically.

Question: How to design a single phase inverter?


7.2 A 28GHz 32-element phased-array transceiver IC with concurrent dual polarized beams and 1.4 degree beam-steering resolution for 5G communication. Next-generation mobile technology (5G) aims to provide an improved experience through higher data-rates, lower latency, and improved link robustness. Millimeter-wave phased arrays offer a path to support multiple users at high data-rates using high-bandwidth directional links between the base station and mobile devices. To realize this vision, a phased-array-based pico-cell must support a large number of precisely controlled beams, yet be compact and power efficient. These system goals have significant mm-wave radio interface implications, including scalability of the RFIC+antenna-array solution, increase in the number of concurrent beams by supporting dual polarization, precise beam steering, and high output power without sacrificing TX power efficiency. Packaged Si-based phased arrays [1–3] with nonconcurrent dual-polarized TX and RX operation [2,3], concurrent dual-polarized RX operation [3] and multi-IC scaling [3,4] have been demonstrated. However, support for concurrent dual-polarized operation in both RX and TX remains unaddressed, and high output power comes at the cost of power consumption, cooling complexity and increased size. The RFIC reported here addresses these challenges. It supports concurrent and independent dual-polarized operation in TX and RX modes, and is compatible with a volume-efficient, scaled, antenna-in-package array. A new TX/RX switch at the shared antenna interface enables high output power without sacrificing TX efficiency, and a t-line-based phase shifter achieves <1° RMS error and <5° phase steps for precise beam control.

Question: What is the size of the canadian military?


Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph. There is high demand for automated tools that assign polarity to microblog content such as tweets (Twitter posts), but this is challenging due to the terseness and informality of tweets in addition to the wide variety and rapid evolution of language in Twitter. It is thus impractical to use standard supervised machine learning techniques dependent on annotated training examples. We do without such annotations by using label propagation to incorporate labels from a maximum entropy classifier trained on noisy labels and knowledge about word types encoded in a lexicon, in combination with the Twitter follower graph. Results on polarity classification for several datasets show that our label propagation approach rivals a model supervised with in-domain annotated tweets, and it outperforms the noisily supervised classifier it exploits as well as a lexicon-based polarity ratio classifier.

Question: What is the polarity of this tweet?


K- NEAREST NEIGHBOR ALGORITHM FOR INSTANCE BASED LEARNING. Instance Based Learning (IBL) results in classifying a new instance by examining and comparing it to the rest of the instances in the dataset. An example of this type of learning is the K-Nearest Neighbor algorithm which is based on examining an average Euclidian distance of the nearest k neighbors' parameters given a certain situation.

Question: What is the K-Nearest Neighbor algorithm?


Visual-Inertial-Semantic Scene Representation for 3D Object Detection. We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones. Inertials afford the ability to impose class-specific scale priors for objects, and provide a global orientation reference. A minimal sufficient representation, the posterior of semantic (identity) and syntactic (pose) attributes of objects in space, can be decomposed into a geometric term, which can be maintained by a localization-and-mapping filter, and a likelihood function, which can be approximated by a discriminatively-trained convolutional neural network The resulting system can process the video stream causally in real time, and provides a representation of objects in the scene that is persistent: Confidence in the presence of objects grows with evidence, and objects previously seen are kept in memory even when temporarily occluded, with their return into view automatically predicted to prime re-detection.

Question: How does the system detect objects in three-dimensional space using video and inertial sensors?


Efficient matrix completion for seismic data reconstruction. Despite recent developments in improved acquisition, seismic data often remains undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We interpret the missing-trace interpolation problem in the context of matrix completion and outline three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We employ an optimization program that restores this low rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we show experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by employing the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to reduce the overall singular values. Simultaneously, in order to work with computationally feasible algorithms for large scale data, we use a factorization-based approach to matrix completion, which significantly speeds up the computations compared to repeated singular value decompositions without reducing the recovery quality. In the context of our theory and experiments, we also show that windowing the data too aggressively can have adverse effects on the recovery quality. To overcome this problem, we carry out our interpolations for each frequency independently while working with the entire frequency slice. The result is a computationally efficient, theoretically motivated framework for interpolating missing-trace data. Our tests on realistic twoand three-dimensional seismic data sets show that our method compares favorably, both in terms of computational speed and recovery quality, to existing curvelet-based and tensor-ba

Question: What is the difference between the Canadian and the US military?


Predicting IMDB Movie Ratings Using Social Media. We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extract textual features from each channel to use in our prediction model and we explore whether data from either of these channels can help to extract a better set of textual feature for prediction. Our best performing model is able to rate movies very close to the observed values.

Question: How to predict IMDB movie ratings?


Real-time traffic sign recognition from video by class-specific discriminative features. Article history: Received 1 August 2007 Received in revised form 22 May 2009 Accepted 26 May 2009

Question: How to recognize traffic signs from video?


Support Vector Machine-recursive feature elimination for the diagnosis of Parkinson disease based on speech analysis. Parkinson disease has become a serious problem in the old people. There is no precise method to diagnosis Parkinson disease now. Considering the significance and difficulty of recognizing the Parkinson disease, the measurement of samples' voices is regard as one of the best non-invasive ways to find the real patient. Support Vector Machine is one of the most effective tools to classify in machine learning, and it has been applied successfully in many areas. In this paper, we implement the SVM-recursive feature elimination which has not been used before for selecting the subset including the most important features for classification from the original features. We also implement SVM with PCA for selecting the principle components for diagnosis PD set with 22 features in order to compare. At last, we discuss the relationship between SVM-RFE and SVM with PCA specially in the experiment. The experiment illustrates that the SVM-RFE has the better performance than other methods in general.

Question: What is the relationship between SVM-RFE and SVM with PCA?


Detailed 3D Representations for Object Recognition and Modeling. Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.

Question: What is the difference between a bounding box and a 3D model?


Relation Extraction Using Multi-Encoder LSTM Network on a Distant Supervised Dataset. Relation extraction techniques are used to find potential relational facts in textual content. Relation Extraction systems require huge amount of training data to model semantics of sentences and identify relations. Distant supervision, often used to construct training data for relation extraction, produces noisy alignments that can hurt the performance of relation extraction systems. To this end, we propose a simple, yet effective, technique to automatically compute confidence levels of alignments. We compute the confidence values of automatically labeled content using co-occurrence statistics of relations and dependency patterns of aligned sentences. Thereafter, we propose a novel multi-encoder bidirectional Long Short Term Memory (LSTM) model to identify relations in a given sentence. We use different features (words, part-of-speech (POS) tags and dependency paths) in each encoder and concatenate the hidden states of all the encoders to predict the relations. Our experiments show that a multi-encoder network can handle different features together to predict relations more accurately (~9% improvement over a single encoder model). We also conduct visualization experiments to show that our model learns intermediate representations effectively to identify relations in sentences.

Question: What is the relation extraction technique?


Accurate Angle Estimator for High-Frame-Rate 2-D Vector Flow Imaging. This paper presents a novel approach for estimating 2-D flow angles using a high-frame-rate ultrasound method. The angle estimator features high accuracy and low standard deviation (SD) over the full 360° range. The method is validated on Field II simulations and phantom measurements using the experimental ultrasound scanner SARUS and a flow rig before being tested in vivo. An 8-MHz linear array transducer is used with defocused beam emissions. In the simulations of a spinning disk phantom, a 360° uniform behavior on the angle estimation is observed with a median angle bias of 1.01° and a median angle SD of 1.8°. Similar results are obtained on a straight vessel for both simulations and measurements, where the obtained angle biases are below 1.5° with SDs around 1°. Estimated velocity magnitudes are also kept under 10% bias and 5% relative SD in both simulations and measurements. An in vivo measurement is performed on a carotid bifurcation of a healthy individual. A 3-s acquisition during three heart cycles is captured. A consistent and repetitive vortex is observed in the carotid bulb during systoles.

Question: What is the angle estimator for high-frame-rate 2-D vector flow imaging?


Boosting Recurrent Neural Networks for Time Series Prediction. We adapt a boosting algorithm to the problem of predicting future values of time series, using recurrent neural networks as base learners. The experiments we performed show that boosting actually provides improved results and that the weighted median is better for combining the learners than the weighted mean.

Question: What is the weighted median?


A Survey of Recent Results in Networked Control Systems. Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies

Question: What is a tutorial?


Modeling a DC Power System in Hardware-inthe-Loop Environment. ii Abstract (in Finnish) iiiin Finnish) iii

Question: How to model a DC power system in hardware-in-the-loop environment?


Stigma and its public health implications.

Question: What is stigma?


Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor. The use of the commercial video games as rehabilitation tools, such as the Nintendo WiiFit, has recently gained much interest in the physical therapy arena. Motion tracking controllers such as the Nintendo Wiimote are not sensitive enough to accurately measure performance in all components of balance. Additionally, users can figure out how to "cheat" inaccurate trackers by performing minimal movement (e.g. wrist twisting a Wiimote instead of a full arm swing). Physical rehabilitation requires accurate and appropriate tracking and feedback of performance. To this end, we are developing applications that leverage recent advances in commercial video game technology to provide full-body control of animated virtual characters. A key component of our approach is the use of newly available low cost depth sensing camera technology that provides markerless full-body tracking on a conventional PC. The aim of this research was to develop and assess an interactive game-based rehabilitation tool for balance training of adults with neurological injury.

Question: How to develop a game-based balance rehabilitation tool using the microsoft kinect sensor?


An 82.4% efficiency package-bondwire-based four-phase fully integrated buck converter with flying capacitor for area reduction. Multi-phase converters have become a topic of great interest due to the high output power capacity and output ripple cancellation effect. They are even more beneficial to nowadays high-frequency fully integrated converters with output capacitor integrated on-chip. As one of the dominant chip area consumers, reducing the size of the on-chip decoupling capacitors directly leads to cost down. It is reported that a 5× capacitor area reduction can be achieved with a four-phase converter compared to a single-phase one [1]. However, the penalty is obvious. Every extra phase comes with an inductor, which is also counted as cost and becomes more dominant with increase in the number of phases.

Question: What is the size of the on-chip decoupling capacitors?


Ultrafast photonic reinforcement learning based on laser chaos. Reinforcement learning involves decision making in dynamic and uncertain environments and constitutes an important element of artificial intelligence (AI). In this work, we experimentally demonstrate that the ultrafast chaotic oscillatory dynamics of lasers efficiently solve the multi-armed bandit problem (MAB), which requires decision making concerning a class of difficult trade-offs called the exploration–exploitation dilemma. To solve the MAB, a certain degree of randomness is required for exploration purposes. However, pseudorandom numbers generated using conventional electronic circuitry encounter severe limitations in terms of their data rate and the quality of randomness due to their algorithmic foundations. We generate laser chaos signals using a semiconductor laser sampled at a maximum rate of 100 GSample/s, and combine it with a simple decision-making principle called tug of war with a variable threshold, to ensure ultrafast, adaptive, and accurate decision making at a maximum adaptation speed of 1 GHz. We found that decision-making performance was maximized with an optimal sampling interval, and we highlight the exact coincidence between the negative autocorrelation inherent in laser chaos and decision-making performance. This study paves the way for a new realm of ultrafast photonics in the age of AI, where the ultrahigh bandwidth of light wave can provide new value.

Question: What is the maximum adaptation speed of a decision-making algorithm?


Comparison between JSON and XML in Applications Based on AJAX. As the core technology of Web 2.0, Ajax has caught more and more attention. Xml, as the traditional data load format, needs to be resolved by DOM (Document Object Model ) both in client-side and server-side, which wastes system resource and makes a great reduction of user-friendliness. In this paper, a light-weightdata-interchanging format-JSON (Java Script Object Notation) will be introduced, which provides a higher level of flexibility and efficiency. We make a comparison between JSON and XML through expriment, then use JSON as data-transfering format in an actual project. Results show that JSON is more suitable as a data-loading tool for Ajax applications.

Question: What is the difference between JSON and XML?


CVAP: Validation for Cluster Analyses. Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work. We present a visual cluster validation tool, the Cluster Validity Analysis Platform (CVAP), to facilitate cluster validation. The CVAP provides necessary methods (e.g., many validity indices, several clustering algorithms and procedures) and an analysis environment for clustering, evaluation of clustering results, estimation of the number of clusters, and performance comparison among different clustering algorithms. It can help users accomplish their clustering tasks faster and easier and help achieve good clustering quality when there is little prior knowledge about the cluster structure of a data set.

Question: How to validate a clustering result?


CAVE-SOM: Immersive visual data mining using 3D Self-Organizing Maps. Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework - CAVE-SOM. The CAVE-SOM system couples the Self-Organizing Map (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.

Question: What is a self-organizing map?


Brain activation modulated by sentence comprehension.. The comprehension of visually presented sentences produces brain activation that increases with the linguistic complexity of the sentence. The volume of neural tissue activated (number of voxels) during sentence comprehension was measured with echo-planar functional magnetic resonance imaging. The modulation of the volume of activation by sentence complexity was observed in a network of four areas: the classical left-hemisphere language areas (the left laterosuperior temporal cortex, or Wernicke's area, and the left inferior frontal gyrus, or Broca's area) and their homologous right-hemisphere areas, although the right areas had much smaller volumes of activation than did the left areas. These findings generally indicate that the amount of neural activity that a given cognitive process engenders is dependent on the computational demand that the task imposes.

Question: How does sentence comprehension affect brain activation?


Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation. Despite of the progress achieved by deep learning in face recognition (FR), more and more people find that racial bias explicitly degrades the performance in realistic FR systems. Facing the fact that existing training and testing databases consist of almost Caucasian subjects, there are still no independent testing databases to evaluate racial bias and even no training databases and methods to reduce it. To facilitate the research towards conquering those unfair issues, this paper contributes a new dataset called Racial Faces in-the-Wild (RFW) database with two important uses, 1) racial bias testing: four testing subsets, namely Caucasian, Asian, Indian and African, are constructed, and each contains about 3000 individuals with 6000 image pairs for face verification, 2) racial bias reducing: one labeled training subset with Caucasians and three unlabeled training subsets with Asians, Indians and Africans are offered to encourage FR algorithms to transfer recognition knowledge from Caucasians to other races. For we all know, RFW is the first database for measuring racial bias in FR algorithms. After proving the existence of domain gap among different races and the existence of racial bias in FR algorithms, we further propose a deep information maximization adaptation network (IMAN) to bridge the domain gap, and comprehensive experiments show that the racial bias could be narrowed-down by our algorithm.

Question: What is the first large-scale Canadian peacekeeping mission?


To Kill a Centrifuge A Technical Analysis of What Stuxnet ' s Creators Tried to Achieve.

Question: What is a centrifuge?


Autonomous Underwater Vehicles : Trends and Transformations. Three examples of inter-agency cooperation utilizing current generation, individual Autonomous Underwater Vehicles (AUVs) are described consistent with recent recommendations of the U.S. Commission on Ocean Policy. The first steps in transforming individual AUVs into adaptive, networked systems are underway. To realize an affordable and deployable system, a network-class AUV must be designed with cost–size constraints not necessarily applied in developing solo AUVs. Vehicle types are suggested based on function and ocean operating regime: surface layer, interior and bottom layer. Implications for platform, navigation and control subsystems are explored and practical formulations for autonomy and intelligence are postulated for comparing performance and judging behavior. Laws and conventions governing intelligent maritime navigation are reviewed and an autonomous controller with conventional collision avoidance behavior is described. Network-class cost constraints can be achieved through economies of scale. Productivity and efficiency in AUV manufacturing will increase if constructive competition is maintained. Constructive strategies include interface and operating standards. Professional societies and industry trade groups have a leadership role to play in establishing public, open standards. cations are described at many conferences and in an expanding literature of journal publications. Griffiths (2003) provides recent developments in AUV design, construction and operation. A number of commercial manufacturers have emerged to supply the growing market. Clearly, individual AUVs are evolving into useful tools that extend current measurement methods. Three examples involving current generation, individual AUVs will serve to illustrate trends in inter-agency cooperation utilizing this technology. Following these examples, we examine factors that will transform current measurement methods. Network externalit

Question: What is the difference between a solo and a network-class AUV?


A Winnow-Based Approach to Context-Sensitive Spelling Correction. A large class of machine-learning problems in natural language require the characterization of linguistic context. Two characteristic properties of such problems are that their feature space is of very high dimensionality, and their target concepts depend on only a small subset of the features in the space. Under such conditions, multiplicative weight-update algorithms such as Winnow have been shown to have exceptionally good theoretical properties. In the work reported here, we present an algorithm combining variants of Winnow and weighted-majority voting, and apply it to a problem in the aforementioned class: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a statistics-based method representing the state of the art for this task. We find: (1) When run with a full (unpruned) set of features, WinSpell achieves accuracies significantly higher than BaySpell was able to achieve in either the pruned or unpruned condition; (2) When compared with other systems in the literature, WinSpell exhibits the highest performance; (3) While several aspects of WinSpell's architecture contribute to its superiority over BaySpell, the primary factor is that it is able to learn a better linear separator than BaySpell learns; (4) When run on a test set drawn from a different corpus than the training set was drawn from, WinSpell is better able than BaySpell to adapt, using a strategy we will present that combines supervised learning on the training set with unsupervised learning on the (noisy) test set.

Question: What is the state of the art for context-sensitive spelling correction?


Hybris: Robust Hybrid Cloud Storage. Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources.  Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data which is dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages, but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f + 1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3f + 1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds.  We implemented Hybris in Java and evaluated it using a series of micro and macrobenchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.

Question: What is the size of the canadian military?


Autonomous railway crack detector robot for bangladesh: SCANOBOT. An automatic railway track crack detector system for Bangladesh Railway has been proposed here which aims in building a robot that can detect and analyze any kind of crack on the railway line and send the coordinates of that faulty line to the concerned authority. This robot includes two ultrasonic sensors, GPS, GSM modules, and Arduino Mega based crack detection assembly which is cost effective and robust to facilitate better safety standards in railways. As soon as the robot passed through a crack that might cause the derailment of a train, the ultrasonic sensors sense that and generate a signal. Then this signal is fed into the Arduino Mega. At that point, with the assistance of GSM and GPS modules, an alert SMS consist of the geographic coordinate of that damaged track is sent to the nearby railway authority who can easily take necessary steps to resolve the problem before any major accident occurs. This will save several trains in Bangladesh from an unwanted discontinuity from the rail track.

Question: What is the purpose of this robot?


Client-Server Computing in Mobile Environments. Recent advances in wireless data networking and portable information appliances have engendered a new paradigm of computing, called mobile computing, in which users carrying portable devices have access to data and information services regardless of their physical location or movement behavior. In the meantime, research addressing information access in mobile environments has proliferated. In this survey, we provide a concrete framework and categorization of the various ways of supporting mobile client-server computing for information access. We examine characteristics of mobility that distinguish mobile client-server computing from its traditional counterpart. We provide a comprehensive analysis of new paradigms and enabler concepts for mobile client-server computing, including mobile-aware adaptation, extended client-server model, and mobile data access. A comparative and detailed review of major research prototypes for mobile information access is also presented.

Question: What is the difference between mobile client-server computing and traditional client-server computing?


Ruminating Reader: Reasoning with Gated Multi-Hop Attention. To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BIDAF). We propose novel layer structures that construct a query aware context vector representation and fuse encoding representation with intermediate representation on top of BIDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BIDAF baseline by 2.1 F1 score and 2.7 EM score. Our analysis shows that different hops of the attention have different responsibilities in selecting answers.

Question: What is the difference between the single-pass and multi-pass models?


Cooperative Sensing among Cognitive Radios. Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.

Question: How many cognitive radios are there in the world?


A Fast Algorithm for Finding Dominators in a Flowgraph. A fast algorithm for finding dominators in a flowgraph is presented. The algorithm uses depth-first search and an efficient method of computing functions defined on paths in trees. A simple implementation of the algorithm runs in <italic>O</italic>(<italic>m</italic> log <italic>n</italic>) time, where <italic>m</italic> is the number of edges and <italic>n</italic> is the number of vertices in the problem graph. A more sophisticated implementation runs in <italic>O</italic>(<italic>m</italic>α(<italic>m</italic>, <italic>n</italic>)) time, where α(<italic>m</italic>, <italic>n</italic>) is a functional inverse of Ackermann's function. Both versions of the algorithm were implemented in Algol W, a Stanford University version of Algol, and tested on an IBM 370/168. The programs were compared with an implementation by Purdom and Moore of a straightforward <italic>O</italic>(<italic>mn</italic>)-time algorithm, and with a bit vector algorithm described by Aho and Ullman. The fast algorithm beat the straightforward algorithm and the bit vector algorithm on all but the smallest graphs tested.

Question: What is a dominator?


Rethinking Visualization: A High-Level Taxonomy. We present the novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user's conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relates and overlap, and that may inspire research ideas in hybrid visualization areas

Question: What is a high-level taxonomy?


PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector. In this paper, we address the task of instance-level semantic boundary detection. To this end, we generate a large database consisting of more than 10k images (which is 20 bigger than existing edge detection databases) along with ground truth boundaries between 459 semantic classes including instances from both foreground objects and different types of background, and call it the PASCAL Boundaries dataset. This is a timely introduction of the dataset since results on the existing standard dataset for measuring edge detection performance, i.e. BSDS500, have started to saturate. In addition to generating a new dataset, we propose a deep network-based multi-scale semantic boundary detector and name it Multi-scale Deep Semantic Boundary Detector (M-DSBD). We evaluate M-DSBD on PASCAL boundaries and compare it to baselines. We test the transfer capabilities of our model by evaluating on MS-COCO and BSDS500 and show that our model transfers well.

Question: What is the size of the dataset?


Autonomous lift of a cable-suspended load by an unmanned aerial robot. In this paper, we address the problem of lifting from the ground a cable-suspended load by a quadrotor aerial vehicle. Furthermore, we consider that the mass of the load is unknown. The lift maneuver is a critical step before proceeding with the transportation of a given cargo. However, it has received little attention in the literature so far. To deal with this problem, we break down the lift maneuver into simpler modes which represent the dynamics of the quadrotor-load system at particular operating regimes. From this decomposition, we obtain a series of waypoints that the aerial vehicle has to reach to accomplish the task. We combine geometric control with a least-squares estimation method to design an adaptive controller that follows a prescribed trajectory planned based on the waypoints. The effectiveness of the proposed control scheme is demonstrated by numerical simulations.

Question: How to lift a cable-suspended load by a quadrotor?


The business model concept: theoretical underpinnings and empirical illustrations. Received: 13 December 2001 Revised: 27 March 2002 : 26 July 2002 Accepted:15 October 2002 Abstract The business model concept is becoming increasingly popular within IS, management and strategy literature. It is used within many fields of research, including both traditional strategy theory and in the emergent body of literature on e-business. However, the concept is often used independently from theory, meaning model components and their interrelations are relatively obscure. Nonetheless, we believe that the business model concept is useful in explaining the relation between IS and strategy. This paper offers an outline for a conceptual business model, and proposes that it should include customers and competitors, the offering, activities and organisation, resources and factor market interactions. The causal inter-relations and the longitudinal processes by which business models evolve should also be included. The model criticises yet draws on traditional strategy theory and on the literature that addresses business models directly. The business model is illustrated by an ERP implementation in a European multi-national company. European Journal of Information Systems (2003) 12, 49–59. doi:10.1057/ palgrave.ejis.3000446

Question: What is the business model concept?


Make Evasion Harder: An Intelligent Android Malware Detection System. To combat the evolving Android malware attacks, in this paper, instead of only using Application Programming Interface (API) calls, we further analyze the different relationships between them and create higher-level semantics which require more efforts for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning to make predictions. Promising experimental results based on real sample collections from Comodo Cloud Security Center demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.

Question: What is the difference between a meta-path and a path?


Ml.lib: robust, cross-platform, open-source machine learning for max and pure data. This paper documents the development of ml.lib: a set of opensource tools designed for employing a wide range of machine learning techniques within two popular real-time programming environments, namely Max and Pure Data. ml.lib is a crossplatform, lightweight wrapper around Nick Gillian's Gesture Recognition Toolkit, a C++ library that includes a wide range of data processing and machine learning techniques. ml.lib adapts these techniques for real-time use within popular dataflow IDEs, allowing instrument designers and performers to integrate robust learning, classification and mapping approaches within their existing workflows. ml.lib has been carefully designed to allow users to experiment with and incorporate machine learning techniques within an interactive arts context with minimal prior knowledge. A simple, logical and consistent, scalable interface has been provided across over sixteen externals in order to maximize learnability and discoverability. A focus on portability and maintainability has enabled ml.lib to support a range of computing architectures—including ARM— and operating systems such as Mac OS, GNU/Linux and Windows, making it the most comprehensive machine learning implementation available for Max and Pure Data.

Question: What is a dataflow?


Location Assisted Coding (LAC): Embracing Interference in Free Space Optical Communications. As the number of wireless devices grows, the increasing demand for the shared radio frequency (RF) spectrum becomes a critical problem. Unlike wired communications in which, theoretically, more fibers can be used to accommodate the increasing bandwidth demand, wireless spectrum cannot be arbitrarily increased due to the fundamental limitations imposed by the physical laws. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. However, high-speed FSO technologies are currently confined to short distance transmissions, resulting in limited mobility. In this paper, we briefly describe WiFO, a hybrid WiFi-FSO network for Gbps wireless local area network (WLAN) femtocells that can provide up to one Gbps per user while maintaining seamless mobility. While typical RF femtocells are non-overlapped to minimize inter-cell interference, there are advantages of using overlapped femtocells to increase mobility and throughput when the number of users is small. That said, the primary contribution of this paper will be a novel location assisted coding (LAC) technique used in the WiFO network that aims to increase throughput and reduce interference for multiple users in a dense array of femtocells. Both theoretical analysis and numerical experiments show orders of magnitude increase in throughput using LAC over basic codes.

Question: What is the difference between a femtocell and a picocell?


Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. We introduce a method to train Binarized Neural Networks (BNNs) neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. To validate the effectiveness of BNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on-line.

Question: How to train a binarized neural network?


Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination.. Multiple sclerosis (MS) is a disease with profound heterogeneity in clinical course, neuroradiological appearance of the lesions, involvement of susceptibility gene loci, and response to therapy. These features are supported by experimental evidence, which demonstrates that fundamentally different processes, such as autoimmunity or virus infection, may induce MS-like inflammatory demyelinating plaques and suggest that MS may be a disease with heterogeneous pathogenetic mechanisms. From a large pathology sample of MS, collected in three international centers, we selected 51 biopsies and 32 autopsies that contained actively demyelinating lesions defined by stringent criteria. The pathology of the lesions was analyzed using a broad spectrum of immunological and neurobiological markers. Four fundamentally different patterns of demyelination were found, defined on the basis of myelin protein loss, the geography and extension of plaques, the patterns of oligodendrocyte destruction, and the immunopathological evidence of complement activation. Two patterns (I and II) showed close similarities to T-cell-mediated or T-cell plus antibody-mediated autoimmune encephalomyelitis, respectively. The other patterns (III and IV) were highly suggestive of a primary oligodendrocyte dystrophy, reminiscent of virus- or toxin-induced demyelination rather than autoimmunity. At a given time point of the disease--as reflected in autopsy cases--the patterns of demyelination were heterogeneous between patients, but were homogenous within multiple active lesions from the same patient. This pathogenetic heterogeneity of plaques from different MS patients may have fundamental implications for the diagnosis and therapy of this disease.

Question: What is the pathogenesis of multiple sclerosis?


"Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent. Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly converses with end users over time by recruiting workers on demand, which in turn decide what might be the best response for each user sentence. Up to the first month of our deployment, 59 users have held conversations with Chorus during 320 conversational sessions. In this paper, we present an account of Chorus' deployment, with a focus on four challenges: (i) identifying when conversations are over, (ii) malicious users and workers, (iii) on-demand recruiting, and (iv) settings in which consensus is not enough. Our observations could assist the deployment of crowd-powered conversation systems and crowd-powered systems in general.

Question: What is the crowd-powered conversational assistant?


Yet another DSL for cross-platforms mobile development. With the growing success of mobility, mobile platforms (iOS, Android, WindowsPhone, etc.) multiply, each requiring specific development skills. Given this situation, it becomes very difficult for software developers to duplicate their apps accordingly. Meanwhile, web-based applications have evolved to be "mobile-friendly" but it appears that this is not a silver bullet: the user experience and the overall quality is still better with native applications. Of course, cross-platform mobile development tools have emerged in recent years. This paper provides a survey of these tools and points out that a full-fledged language for mobile development is highly desirable. Consequently, we present a preliminary work on Xmob, a technology-neutral DSL intended to be cross-compiled to produce native code for a variety of platforms.

Question: What is a DSL?


A New Concept Varistor With Epoxy/Microvaristor Composite. A new concept composite varistor is proposed in this paper. This composite varistor has the chains of microvaristors, which are formed by applying an electric field during the curing process and work as current paths. This composite varistor shows superior nonlinear voltage-current characteristics despite small microvaristor content and has a good response against a steep voltage surge of several tens of nanoseconds in front time. In comparison with ceramic varistors where shapes are usually formed at their sintering temperatures of around 1000°C, the composite varistor has an advantage in flexible shape because its shape is formed by curing the base polymer at a low temperature.

Question: What is a varistor?


Machine Learning in Medical Applications. Research in Machine Learning methods to-date remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the workshop were to foster fundamental and applied research in the application of machine learning methods to medical problem solving and to medical research, to provide a forum for reporting significant results, to determine whether Machine Learning methods are able to underpin the research and development on intelligent systems for medical applications, and to identify those areas where increased research is likely to yield advances. A number of recommendations for a research agenda were produced, including both technical and human-centered issues.

Question: What is the goal of the workshop?


Application of Pretrained Deep Neural Networks to Large Vocabulary Speech Recognition. The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently led to a resurgence in the use of Artificial Neural Network Hidden Markov Model (ANN/HMM) hybrid systems for Automatic Speech Recognition (ASR). In this paper we report results of a DBN-pretrained context-dependent ANN/HMM system trained on two datasets that are much larger than any reported previously with DBN-pretrained ANN/HMM systems 5870 hours of Voice Search and 1400 hours of YouTube data. On the first dataset, the pretrained ANN/HMM system outperforms the best Gaussian Mixture Model Hidden Markov Model (GMM/HMM) baseline, built with a much larger dataset by 3.7% absolute WER, while on the second dataset, it outperforms the GMM/HMM baseline by 4.7% absolute. Maximum Mutual Information (MMI) fine tuning and model combination using Segmental Conditional Random Fields (SCARF) give additional gains of 0.1% and 0.4% on the first dataset and 0.5% and 0.9% absolute on the second dataset.

Question: What is the largest dataset used for speech recognition?


Steering with eyes closed: Mm-Wave beam steering without in-band measurement. Millimeter-wave communication achieves multi-Gbps data rates via highly directional beamforming to overcome pathloss and provide the desired SNR. Unfortunately, establishing communication with sufficiently narrow beamwidth to obtain the necessary link budget is a high overhead procedure in which the search space scales with device mobility and the product of the sender-receiver beam resolution. In this paper, we design, implement, and experimentally evaluate Blind Beam Steering (BBS) a novel architecture and algorithm that removes in-band overhead for directional mm-Wave link establishment. Our system architecture couples mm-Wave and legacy 2.4/5 GHz bands using out-of-band direction inference to establish (overhead-free) multi-Gbps mm-Wave communication. Further, BBS evaluates direction estimates retrieved from passively overheard 2.4/5 GHz frames to assure highest mm-Wave link quality on unobstructed direct paths. By removing in-band overhead, we leverage mm-Wave's very high throughput capabilities, beam-width scalability and provide robustness to mobility. We demonstrate that BBS achieves 97.8% accuracy estimating direction between pairing nodes using at least 5 detection band antennas. Further, BBS successfully detects unobstructed direct path conditions with an accuracy of 96.5% and reduces the IEEE 802.11ad beamforming training overhead by 81%.

Question: How does mm-wave communication work?


Bottom-Up Segmentation for Top-Down Detection. In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Our approach allows every detection hypothesis to select a segment (including void), and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. Thus our model ``blends'' between the detector and segmentation models. Since our features can be computed very efficiently given the segments, we maintain the same complexity as the original DPM. We demonstrate the effectiveness of our approach in PASCAL VOC 2010, and show that when employing only a root filter our approach outperforms Dalal & Triggs detector on all classes, achieving 13% higher average AP. When employing the parts, we outperform the original DPM in $19$ out of $20$ classes, achieving an improvement of 8% AP. Furthermore, we outperform the previous state-of-the-art on VOC 2010 test by 4%.

Question: How does bottom-up segmentation help object detection?


On-the-Job Learning with Bayesian Decision Theory. Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing queries decreases. We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled way. Computing the optimal policy is intractable, so we develop an approximation based on Monte Carlo Tree Search. We tested our approach on three datasets—named-entity recognition, sentiment classification, and image classification. On the NER task we obtained more than an order of magnitude reduction in cost compared to full human annotation, while boosting performance relative to the expert provided labels. We also achieve a 8% F1 improvement over having a single human label the whole set, and a 28% F1 improvement over online learning. "Poor is the pupil who does not surpass his master." – Leonardo da Vinci

Question: What is the name of the dataset?


Medicinal and pharmaceutical uses of seaweed natural products: A review. In the last three decades the discovery of metabolites with biological activities from macroalgae has increased significantly. However, despite the intense research effort by academic and corporate institutions, very few products with real potential have been identified or developed. Based on Silverplatter MEDLINE and Aquatic Biology, Aquaculture & Fisheries Resources databases, the literature was searched for natural products from marine macroalgae in the Rhodophyta, Phaeophyta and Chlorophyta with biological and pharmacological activity. Substances that currently receive most attention from pharmaceutical companies for use in drug development, or from researchers in the field of medicine-related research include: sulphated polysaccharides as antiviral substances, halogenated furanones from Delisea pulchra as antifouling compounds, and kahalalide F from a species of Bryopsis as a possible treatment of lung cancer, tumours and AIDS. Other substances such as macroalgal lectins, fucoidans, kainoids and aplysiatoxins are routinely used in biomedical research and a multitude of other substances have known biological activities. The potential pharmaceutical, medicinal and research applications of these compounds are discussed.

Question: What is the potential of seaweed natural products?


Time-frequency Analysis 3.1 Time-frequency Analysis. Let f (t) be a mathematical idealization of some physical signal depending on time t. Perhaps f can be considered as a superposition of oscillating components but these oscillations have to be limited to some finite extension in time. This is a fundamental problem of the Fourier inversion formula which states that, for a well-behaved signal f one has f (t) = ∞ −∞ˆf (ξ)e 2πitξ dξ expressing a complex signal f as a superposition of exponentials e 2πitξ. If f vanishes outside some finite set then the exponentials, which extend over all time, must cancel each other in some fantastic way that makes it virtually impossible to quantify in any intuitive way which frequencies play a dominant role at any particular time t. 3.1.2 Frequency Local in time Consider a physical signal to be a square integrable real-valued function of time, x(t). One can define a complex extension z(t) = x(t) + iy(t) bey letting y(t) be the inverse Fourier transform of −isgn (t)ˆ x(ξ) where sgn denotes the signum function ξ/|ξ|. In this casê z = ˆ x + iˆy has only positive frequencies. Exercise 3.1.1. Explain why z(t) has an extension to a complex argument t + is, s > 0 that is analytic in the upper half plane {t + is : s > 0}. You may assume that x(t) is a continuous, bounded, absolutely integrable function. The analytic signal z has the polar form r(t)e iθ(t) where r = x 2 + y 2 and θ = arctan y/x. The instantaneous frequency can be defined as dθ/dt. This point of view however is a little too simple because x(t) can be a superposition of multiple oscillating components and the instantaneous frequency cannot resolve multiple oscillating contributions. We will return to this point later in this chapter. First we want to consider some fundamental issues governing the impossibility of joint time-frequency localization and, in view of these limitations, mathematical tools that aim to characterize compositions of signals in terms of time localized osc

Question: What is the difference between the instantaneous frequency and the instantaneous phase?


Small-footprint keyword spotting using deep neural networks. Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement.

Question: What is a small footprint keyword spotting system?


Optimal integration of energy storage in distribution networks. Energy storage, traditionally well established in the form of large scale pumped-hydro systems, is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the wider uptake of intermittent renewable resources and to distributed generation in general, which are likely to present a whole range of new business opportunities for storage systems and their suppliers. In the paper, by assuming that Distribution System Operator has got the ownership and operation of storage, a new software planning tool for distribution networks able to define the optimal placement, rating and control strategies of distributed storage systems that minimize the overall network cost is proposed. This tool will assist the System Operators in defining the better integration strategies of distributed storage systems in distribution networks and in assessing their potential as an option for a more efficient operation and development of future electricity distribution networks.

Question: What is the optimal integration of energy storage in distribution networks?


Pure Pursuit Revisited: Field Testing of Autonomous Vehicles in Urban Areas. In this paper, we aim to explore path following. We implement a path following component by referring to the existing Pure Pursuit algorithm. Using the simulation and field operational test, we identified the problem in the path following component. The main problems identified were with respect to vehicles meandering off the path, turning a corner, and the instability of steering control. Therefore, we apply some modifications to the Pure Pursuit[1] algorithm. We have also conducted the simulation and field operational tests again to evaluate these modifications.

Question: What is the path following component?


Gain Improvement of Rectangular Dielectric Resonator Antenna by Engraving Grooves on Its Side Walls. A new technique for increasing the boresight gain of a rectangular dielectric resonator antenna (DRA) operating at its fundamental radiating <inline-formula><tex-math notation="LaTeX">$TE_{111}^y$</tex-math></inline-formula> mode is introduced. The idea is to increase the radiations from the side walls of the DRA compared to that of its top wall by engraving grooves on the side walls. A model based on the array theory is developed to explain the high-gain nature of the antenna. The measured results demonstrate that the proposed antenna achieves an impedance bandwidth of 21% over a band of 3.24–4 GHz, with a maximum gain of 9.6 dB. This is significantly higher with respect to available data in the literature.

Question: How does the grooves on the side walls of the DRA increase the radiations from the side walls?


High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study. Mismatched coded excitation (CE) can be employed to increase the frame rate of synthetic aperture ultrasound imaging. The high autocorrelation and low cross correlation (CC) of transmitted signals enables the identification and separation of signal sources at the receiver. Thus, the method provides B-mode imaging with simultaneous transmission from several elements and capability of spatial decoding of the transmitted signals, which makes the imaging process equivalent to consecutive transmissions. Each transmission generates its own image and the combination of all the images results in an image with a high lateral resolution. In this paper, we introduce two different methods for generating multiple mismatched CEs with an identical frequency bandwidth and code length. Therefore, the proposed families of mismatched CEs are able to generate similar resolutions and signal-to-noise ratios. The application of these methods is demonstrated experimentally. Furthermore, several techniques are suggested that can be used to reduce the CC between the mismatched codes.

Question: How can I get a high frame rate?