Query: Modulation Technique for Single-Phase Transformerless Photovoltaic Inverters With Reactive Power Capability

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An Optimized Transformerless Photovoltaic Grid-Connected Inverter <=SEP=> Unipolar sinusoidal pulsewidth modulation (SPWM) full-bridge inverter brings high-frequency common-mode voltage, which restricts its application in transformerless photovoltaic grid-connected inverters. In order to solve this problem, an optimized full-bridge structure with two additional switches and a capacitor divider is proposed in this paper, which guarantees that a freewheeling path is clamped to half input voltage in the freewheeling period. Sequentially, the high-frequency common-mode voltage has been avoided in the unipolar SPWM full-bridge inverter, and the output current flows through only three switches in the power processing period. In addition, a clamping branch makes the voltage stress of the added switches be equal to half input voltage. The operation and clamping modes are analyzed, and the total losses of power device of several existing topologies and proposed topology are fairly calculated. Finally, the common-mode performance of these topologies is compared by a universal prototype inverter rated at 1 kW.

A New High-Efficiency Single-Phase Transformerless PV Inverter Topology <=SEP=> There is a strong trend in the photovoltaic inverter technology to use transformerless topologies in order to acquire higher efficiencies combining with very low ground leakage current. In this paper, a new topology, based on the H-bridge with a new ac bypass circuit consisting of a diode rectifier and a switch with clamping to the dc midpoint, is proposed. The topology is simulated and experimentally validated, and a comparison with other existing topologies is performed. High conversion efficiency and low leakage current are demonstrated.

Comparison and Analysis of Single-Phase Transformerless Grid-Connected PV Inverters <=SEP=> Leakage current minimization is one of the most important considerations in transformerless photovoltaic (PV) inverters. In the past, various transformerless PV inverter topologies have been introduced, with leakage current minimized by the means of galvanic isolation and common-mode voltage (CMV) clamping. The galvanic isolation can be achieved via dc-decoupling or ac-decoupling, for isolation on the dc- or ac-side of the inverter, respectively. It has been shown that the latter provides lower losses due to the reduced switch count in conduction path. Nevertheless, leakage current cannot be simply eliminated by galvanic isolation and modulation techniques, due to the presence of switches' junction capacitances and resonant circuit effects. Hence, CMV clamping is used in some topologies to completely eliminate the leakage current. In this paper, several recently proposed transformerless PV inverters with different galvanic isolation methods and CMV clamping technique are analyzed and compared. A simple modified H-bridge zero-voltage state rectifier is also proposed, to combine the benefits of the low-loss ac-decoupling method and the complete leakage current elimination of the CMV clamping method. The performances of different topologies, in terms of CMV, leakage current, total harmonic distortion, losses and efficiencies are compared. The analyses are done theoretically and via simulation studies, and further validated with experimental results. This paper is helpful for the researchers to choose the appropriate topology for transformerless PV applications and to provide the design principles in terms of common-mode behavior and efficiency.

Leakage Current Analytical Model and Application in Single-Phase Transformerless Photovoltaic Grid-Connected Inverter <=SEP=> Due to the characteristics of low cost and high efficiency, the transformerless photovoltaic (PV) grid-connected inverters have been popularized in the application of solar electric generation system in residential market. Unfortunately, the leakage current through the stray capacitors between the PV array and the ground is harmful. This paper focuses on the leakage current suppressing method, in which all common-mode paths are considered. First, the common-mode analytical model at switching frequency is developed, and the rules of eliminating switching frequency common-mode source are summarized based on this model. The existing full-bridge- and half-bridge-type converters have been analyzed by using the developed model and rules, and then, a new full-bridge-type converter structure and a compensation strategy for half-bridge-type inverter have been presented finally.

A Family of Neutral Point Clamped Full-Bridge Topologies for Transformerless Photovoltaic Grid-Tied Inverters <=SEP=> Transformerless inverter topologies have attracted more attentions in photovoltaic (PV) generation system since they feature high efficiency and low cost. In order to meet the safety requirement for transformerless grid-tied PV inverters, the leakage current has to be tackled carefully. Neutral point clamped (NPC) topology is an effective way to eliminate the leakage current. In this paper, two types of basic switching cells, the positive neutral point clamped cell and the negative neutral point clamped cell, are proposed to build NPC topologies, with a systematic method of topology generation given. A family of single-phase transformerless full-bridge topologies with low-leakage current for PV grid-tied NPC inverters is derived including the existing oH5 and some new topologies. A novel positive-negative NPC (PN-NPC) topology is analyzed in detail with operational modes and modulation strategy given. The power losses are compared among the oH5, the full-bridge inverter with dc bypass (FB-DCBP) topology, and the proposed PN-NPC topologies. A universal prototype for these three NPC-type topologies mentioned is built to evaluate the topologies at conversion efficiency and the leakage current characteristic. The PN-NPC topology proposed exhibits similar leakage current with the FB-DCBP, which is lower than that of the oH5 topology, and features higher efficiency than both the oH5 and the FB-DCBP topologies.

Cyber-Physical System Security of a Power Grid : State-ofthe-Art <=SEP=> As part of the smart grid development, more and more technologies are developed and deployed on the power grid to enhance the system reliability. A primary purpose of the smart grid is to significantly increase the capability of computer-based remote control and automation. As a result, the level of connectivity has become much higher, and cyber security also becomes a potential threat to the cyber-physical systems (CPSs). In this paper, a survey of the state-of-the-art is conducted on the cyber security of the power grid concerning issues of: (1) the structure of CPSs in a smart grid; (2) cyber vulnerability assessment; (3) cyber protection systems; and (4) testbeds of a CPS. At Washington State University (WSU), the Smart City Testbed (SCT) has been developed to provide a platform to test, analyze and validate defense mechanisms against potential cyber intrusions. A test case is provided in this paper to demonstrate how a testbed helps the study of cyber security and the anomaly detection system (ADS) for substations.

The paradox of received social support: the importance of responsiveness. <=SEP=> Although the perception of available support is associated with positive outcomes, the receipt of actual support from close others is often associated with negative outcomes. In fact, support that is "invisible" (not perceived by the support recipient) is associated with better outcomes than "visible" support. To investigate this paradox, we proposed that received support (both visible and invisible) would be beneficial when it was responsive to the recipient's needs. Sixty-seven cohabiting couples participated in a daily-experience study in which they reported on the support they provided and received each day. Results indicated that both visible and invisible support were beneficial (i.e., associated with less sadness and anxiety and with greater relationship quality) only when the support was responsive. These findings suggest that the nature of support is an important determinant of when received support will be beneficial.

The study of evasion of packed PE from static detection <=SEP=> Static detection of packed portable executables (PEs) relies primarily on structural properties of the packed PE, and also on anomalies in the packed PE caused by packing tools. This paper outlines weaknesses in this method of detection. We show that these structural properties and anomalies are contingent features of the executable, and can be more or less easily modified to evade static detection.

BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs <=SEP=> SUMMARY
BAliBASE is a database of manually refined multiple sequence alignments categorized by core blocks of conservation sequence length, similarity, and the presence of insertions and N/C-terminal extensions.


AVAILABILITY
From http://www-igbmc. u-strasbg.fr/BioInfo/BAliBASE/index.html

Pneumatic muscle actuators within robotic and mechatronic systems <=SEP=> Pneumatic artificial muscles (PAMs) as soft, lightweight and compliant actuators have great potential in applications for the actuations of new types of robots and manipulators. The favourable characteristics of fluidic muscles, such as high power-to-weight ratio and safe interaction with humans are also very suitable during the process of musculoskeletally rehabilitating patients and are often used in making artificial orthoses. This technology, despite the problems of control relatng to nonlinear phenomena, may also have wide future applications within industrial and mechatronic systems. This paper presents several experimental systems actuated by PAMs, which have been designed as test models within the fields of mobile robots, mechatronics, fluid power systems and the feedback control education of mechanical engineering students. This paper first presents the design and construction of a four legged walking robot actuated by pneumatic muscles. The robot has a fully autonomous system with a wireless application platform and can be controlled using a cell phone. Then the paper describes the design and construction of the prototype of an actively-powered ankle foot orthosis. This orthosis device actuated by a single PAM is able to provide the appropriate functions required during the rehabilitations of patients and the loss of mobility. Then the paper focuses on the design and control of a ball and beam system with an antagonistic muscle pair for generating the necessary torque for beam rotation. This mechatronic balancing mechanism falls into the category of unstable, under-actuated, multivariable systems with highly nonlinear dynamics. The final section of the article presents the design and control of a single-joint manipulator arm with pneumatic muscle actuators that enable some new features for the controlled systems. Fluid Power 2015 176

Diabetes management in the primary care setting: a comparison of physicians' performance by gender. <=SEP=> BACKGROUND
A major shift in the gender of the medical-doctor workforce is now underway, and all over the world it is expected that an average 65% of the medical workforce will be women by 2030. In addition, an aging population means that chronic diseases, such as diabetes, are becoming more prevalent and the demand for care is rising. There is growing evidence of female physicians performing better than male physicians.AimOur study aimed to investigate whether any differences in diabetes process indicators are associated with gender, and/or the interaction between gender and different organizational models.Design and settingA population-based cross-sectional analysis was conducted on a large data set obtained by processing the public health administration databases of seven Italian local health units (LHUs). The seven LHUs, distributed all over the Italian peninsula in seven different regions, took part in a national project called MEDINA, with the focus on chronic disease management in primary care (PC).


METHODS
A total score was calculated for the average performance in the previously listed five indicators, representing global adherence to a quality management of patients with diabetes. A multilevel analysis was applied to see how LHUs affected the outcome. A quantile regression model was also fitted.


RESULTS
Our study included 2287 Italian general practitioners (586 of them female) caring for a total of 2 646 059 patients. Analyzing the performance scores confirmed that female general practitioners obtained better results than males. The differences between males and females were stronger on the 25th and 75th percentiles of the score than on the median values. The interaction between gender and LHU was not significant.


CONCLUSION
Our study evidenced that female physicians perform better than males in providing PC for diabetes independently by the different organizational models. Further research to understand the reasons for these gender differences is needed.

Direct type-specific conic fitting and eigenvalue bias correction <=SEP=> A new method to fit specific types of conics to scattered data points is introduced. Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure. Fitting of parabolas is achieved by determining an orthogonal basis vector set in the Grassmannian space of the quadratic terms’ coefficients. The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers. This is the first known direct solution for parabola specific fitting. Furthermore, the inherent bias of a linear conic fit is addressed. We propose a linear method of correcting this bias, producing better geometric fits which are still constrained to specific conic type. 2007 Published by Elsevier B.V.

Articulatory feature-based pronunciation modeling <=SEP=> Spoken language, especially conversational speech, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on conversational speech, and it has been very difficult to mitigate in traditional phonebased approaches to speech recognition. An alternative approach, which has been studied by ourselves and others, is one based on sub-phonetic features rather than phones. In such an approach, a word’s pronunciation is represented as multiple streams of phonological features rather than a single stream of phones. Features may correspond to the positions of the speech articulators, such as the lips and tongue, or may be more abstract categories such as manner and place. This article reviews our work on a particular type of articulatory feature-based pronunciation model. The model allows for asynchrony between features, as well as per-feature substitutions, making it more natural to account for many pronunciation changes that are difficult to handle with phone-based models. Such models can be efficiently represented as dynamic Bayesian networks. The feature-based models improve significantly over phone-based counterparts in terms of frame perplexity and lexical access accuracy. The remainder of the article discusses related work and future directions.

Attacking an AES-Enabled NFC Tag: Implications from Design to a Real-World Scenario <=SEP=> Radio-frequency identification (RFID) technology is the enabler for applications like the future internet of things (IoT), where security plays an important role. When integrating security to RFID tags, not only the cryptographic algorithms need to be secure but also their implementation. In this work we present differential power analysis (DPA) and differential electromagnetic analysis (DEMA) attacks on a securityenabled RFID tag. The attacks are conducted on both an ASIC-chip version and on an FPGA-prototype version of the tag. The design of the ASIC version equals that of commercial RFID tags and has analog and digital part integrated on a single chip. Target of the attacks is an implementation of the Advanced Encryption Standard (AES) with 128-bit key length and DPA countermeasures. The countermeasures are shuffling of operations and insertion of dummy rounds. Our results illustrate that the effort for successfully attacking the ASIC chip in a real-world scenario is only 4.5 times higher than for the FPGA prototype in a laboratory environment. This let us come to the conclusion that the effort for attacking contactless devices like RFID tags is only slightly higher than that for contact-based devices. The results further underline that the design of countermeasures like the insertion of dummy rounds has to be done with great care, since the detection of patterns in power or electromagnetic traces can be used to significantly lower the attacking effort.

Building Program Vector Representations for Deep Learning <=SEP=> Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the “coding criterion” to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than “shallow” methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.

A case against convexity in conceptual spaces <=SEP=> The notion of conceptual space, proposed by Gärdenfors as a framework for the representation of concepts and knowledge, has been highly influential over the last decade or so. One of the main theses involved in this approach is that the conceptual regions associated with properties, concepts, verbs, etc. are convex. The aim of this paper is to show that such a constraint—that of the convexity of the geometry of conceptual regions—is problematic; both from a theoretical perspective and with regard to the inner workings of the theory itself. On the one hand, all the arguments provided in favor of the convexity of conceptual regions rest on controversial assumptions. Additionally, his argument in support of a Euclidean metric, based on the integral character of conceptual dimensions, is weak, and under non-Euclidean metrics the structure of regions may be non-convex. Furthermore, even if the metric were Euclidean, the convexity constraint could be not satisfied if concepts were differentially weighted. On the other hand, Gärdenfors’ convexity constraint is brought into question by the own inner workings of conceptual spaces because: (i) some of the allegedly convex properties of concepts are not convex; (ii) the conceptual regions resulting from the combination of convex properties can be non-convex; (iii) convex regions may co-vary in non-convex ways; and (iv) his definition of verbs is incompatible with a definition of properties in terms of convex regions. Therefore, the mandatory character of the convexity requirement for regions in a conceptual space theory should be reconsidered.

Skin microbiota: overview and role in the skin diseases acne vulgaris and rosacea. <=SEP=> As the first barrier to environmental exposures, human skin has developed an integrated immune system to protect the inner body from chemical, physical or microbial insults. Microorganisms inhabiting superficial skin layers are known as skin microbiota and include bacteria, viruses, archaea and fungi. The microbiota composition is crucial in the instruction and support of the skin's immune system. Changes in microbiota can be due to individual, environmental or behavioral factors, such as age, climate, hygiene or antibiotic consumption, which can cause dysbiosis. The contribution of skin microbiota to disease development is known in atopic dermatitis, where there is an increase in Staphylococcus aureus. Culture-independent studies have enabled more accurate descriptions of this complex interplay. Microbial imbalance is associated with the development of various diseases. This review focuses on microbial imbalances in acne vulgaris and rosacea.

Inductive Design of Maturity Models: Applying the Rasch Algorithm for Design Science Research <=SEP=> Maturity models are an established means to systematically document and guide the development of organizations using archetypal capability levels. Often, these models lack a sound foundation and/or are derived on the basis of an arbitrary design method. In order to foster the design of relevant and rigorous artifacts, this paper presents a method for maturity model construction that applies the Rasch algorithm and cluster analysis as a sound methodical foundation. The Rasch algorithm is widely used to improve scholarly intelligence and attainment tests. In order to demonstrate the application of the proposed method and to evaluate its usability and applicability, we present a design exemplar in the business intelligence domain.

Increased prefrontal and parietal activity after training of working memory <=SEP=> Working memory capacity has traditionally been thought to be constant. Recent studies, however, suggest that working memory can be improved by training. In this study, we have investigated the changes in brain activity that are induced by working memory training. Two experiments were carried out in which healthy, adult human subjects practiced working memory tasks for 5 weeks. Brain activity was measured with functional magnetic resonance imaging (fMRI) before, during and after training. After training, brain activity that was related to working memory increased in the middle frontal gyrus and superior and inferior parietal cortices. The changes in cortical activity could be evidence of training-induced plasticity in the neural systems that underlie working memory.

Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach <=SEP=> Aerial images are subject to various types of noise, which restricts the recognition and analysis of images, target monitoring, and search services. At present, deep learning is successful in image recognition. However, traditional convolutional neural networks (CNNs) extract the main features of an image to predict directly and are limited by the requirements of the training sample size (i.e., a small data size is not successful enough). In this paper, using a small sample size, we propose an aerial-image denoising recognition model based on CNNs with a multi-scale residual learning approach. The proposed model has the following three advantages: (1) Instead of directly learning latent clean images, the proposed model learns the noise from noisy images and then subtracts the learned residual from the noisy images to obtain reconstructed (denoised) images; (2) The developed image denoising recognition model is beneficial to small training datasets; (3) We use multi-scale residual learning as a learning approach, and dropout is introduced into the model architecture to force the network to learn to generalize well enough. Our experimental results on aerial-image denoising recognition reveal that the proposed approach is highly superior to the other state-of-the-art methods.

Taking the Human Out of the Loop: A Review of Bayesian Optimization <=SEP=> Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

RECONCEPTUALISING GAMIFICATION : PLAY AND PEDAGOGY <=SEP=> Gamification is a complex and controversial concept. It has been both embraced as a marketing and education revolution, and dismissed as practice of exploitation. Contested within the debate around gamification has been the very concept of what a game is, what the core mechanics of games are, and whether gamification truly mobilises these core mechanics. This paper will challenge the foundation of this debate through reconceptualising gamification not as a simple set of techniques and mechanics, but as a pedagogic heritage, an alternative framework for training and shaping participant behaviour that has at its core the concepts of entertainment and engagement. In doing so it will recontextualise current practices of gamification into a longer and deeper history, and suggest potential pathways for more sophisticated gamification in the future.

Minimum Near-Convex Shape Decomposition. <=SEP=> Shape decomposition is a fundamental problem for part-based shape representation. We propose the Minimum Near-Convex Decomposition (MNCD) to decompose arbitrary shapes into minimum number of &#x0022;near-convex&#x0022; parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of non-intersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via Binary Integer Linear Programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts, and thus leads to robust shape representation.

Recovering the interleaver of an unknown turbo-code <=SEP=> We give here an efficient algorithm for recovering the permutation of an unknown turbo-code when several noisy codewords are given. The algorithm presented here uses the same information as some other algorithms given previously for this problem but in an optimal fashion. This paper also clarifies the link between this problem and the BCJR decoding algorithm.

A 3-kW Wireless Power Transfer System for Sightseeing Car Supercapacitor Charge <=SEP=> Wireless power transfer (WPT) is the preferred charging method for electric vehicles (EVs) powered by battery and supercapacitor. In this paper, a novel WPT system with constant current charging capability for sightseeing car with supercapacitor storage is designed. First, an optimized magnetic coupler using ferrite cores and magnetic shielding structure is proposed to ensure stable power transfer and high efficiency. Compared with the traditional planar shape ferrite core coupler, the proposed magnetic coupler requires lesser ferrite material without degrading the performance of the WPT system. Second, the model of supercapacitor is applied to the WPT system and the relationship between equivalent load resistances of supercapacitor and charging time is analyzed in detail. Then, a Buck converter with proportional integral (PI) controller is implemented on the secondary side to maintain constant charging current for the variable load. Finally, the proposed design is verified by experiments. Constant charging current of 31.5 A across transfer distance of 15 cm is achieved. The peak transfer power and system efficiency are 2.86 kW and 88.05%, respectively.

Step Climbing Omnidirectional Mobile Robot with Passive Linkages <=SEP=> In this paper, we develop a holonomic omni-directional mobile vehicle with step-climbing ability. This system realizes omni-directional motion on flat floor using special wheels and passes over the step in forward or backward direction using the passive linkage mechanism. Our key ideas are the following topics. First topic is new omnidirectional mobile mechanism which consists of special wheels and passive linkage mechanism. Second topic is new passive linkage mechanism which can change its body configuration easily when the vehicle passes over the step. Third topic is wheel control reference derivation based on the body configuration, which changes passively during step climbing for reducing wheel slippage. Last topic is wheel control method which keeps the rotation velocity coordination among the wheels for reducing wheel slippage and increasing the step-climbing performance. We verified the effectiveness of our proposed method by the computer simulations and experiments. Utilizing our proposed mechanism and control systems, the vehicle has both omnidirectional mobility and step overcoming function. Furthermore, our developing vehicle can pass over the 128[mm] height step using the wheel which radius is 66[mm]. Its performance is three times larger than one of general wheeled vehicle.

Pay Attention to the Ending: Strong Neural Baselines for the ROC Story Cloze Task <=SEP=> We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings. We develop a model that uses hierarchical recurrent networks with attention to encode the sentences in the story and score candidate endings. By discarding the large training set and only training on the validation set, we achieve an accuracy of 74.7%. Even when we discard the story plots (sentences before the ending) and only train to choose the better of two endings, we can still reach 72.5%. We then analyze this “ending-only” task setting. We estimate human accuracy to be 78% and find several types of clues that lead to this high accuracy, including those related to sentiment, negation, and general ending likelihood regardless of the story context.

Convenient or Useful ? Consumer Adoption of Smartphones for Mobile Commerce <=SEP=> Merchants have developed apps for the smartphone that assist consumers through the buying process, from when they gather information to when they decide to purchase. Sharing information, such as location, shopping preferences and financial data, can enhance consumers’ experience. However, they may be reluctant to disclose these details unless they perceive that the benefits gained are more than the risk of privacy loss. This privacy calculus is added to the unified theory of acceptance and use of technology (UTAUT2) in order to explain consumers’ willingness to exchange the disclosure of personal information for additional value. Sharing information makes mobile commerce more convenient by saving time and effort. Companies are able to send offers that are tailored to a specific customer. Payments are processed faster because the merchant already has the financial data on hand. UTAUT2 is further extended with the Theory of Convenience. Results from a survey of over 300 consumers show that perceived value and perceived convenience are influencing variables and that perceived value mediates the influence of perceived convenience on intention to use.

DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer <=SEP=> Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto’s service for marketers to track their brands in user-generated images, and LogoGrab’s mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.

Hidden Markov Models for Speech Recognition <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.