Query: Will the Pedestrian Cross? A Study on Pedestrian Path Prediction

Relevant passages:
Monocular Pedestrian Detection: Survey and Experiments <=SEP=> Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.

3D People Tracking with Gaussian Process Dynamical Models <=SEP=> We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of human motion data, with a density function that gives higher probability to poses and motions close to the training data. With Bayesian model averaging a GPDM can be learned from relatively small amounts of data, and it generalizes gracefully to motions outside the training set. Here we modify the GPDM to permit learning from motions with significant stylistic variation. The resulting priors are effective for tracking a range of human walking styles, despite weak and noisy image measurements and significant occlusions.

Probabilistic Principal Component Analysis <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. 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.

Fast Human Detection Using a Cascade of Histograms of Oriented Gradients <=SEP=> We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.

Pattern Recognition and Machine Learning <=SEP=> The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. C. Bishop Pattern Recognition and Machine Learning

Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease <=SEP=> The pathophysiological process of Alzheimer's disease (AD) is thought to begin many years before the diagnosis of AD dementia. This long "preclinical" phase of AD would provide a critical opportunity for therapeutic intervention; however, we need to further elucidate the link between the pathological cascade of AD and the emergence of clinical symptoms. The National Institute on Aging and the Alzheimer's Association convened an international workgroup to review the biomarker, epidemiological, and neuropsychological evidence, and to develop recommendations to determine the factors which best predict the risk of progression from "normal" cognition to mild cognitive impairment and AD dementia. We propose a conceptual framework and operational research criteria, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies. These recommendations are solely intended for research purposes and do not have any clinical implications at this time. It is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD, and ultimately, aid the field in moving toward earlier intervention at a stage of AD when some disease-modifying therapies may be most efficacious.

Flexible HALS algorithms for sparse non-negative matrix/tensor factorization <=SEP=> In this paper we propose a family of new algorithms for non-negative matrix/tensor factorization (NMF/NTF) and sparse nonnegative coding and representation that has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. We have developed a class of local algorithms which are extensions of hierarchical alternating least squares (HALS) algorithms proposed by us in . For these purposes, we have performed simultaneous constrained minimization of a set of robust cost functions called alpha and beta divergences. Our algorithms are locally stable and work well for the NMF blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, new algorithms can be potentially accommodated to different noise statistics by just adjusting a single parameter. Extensive experimental results confirm the validity and high performance of the developed algorithms, especially, with usage of the multi-layer hierarchical approach .

We don't need another hero. <=SEP=> Everybody loves the stories of heroes like Martin Luther King, Jr., Mother Teresa, and Gandhi. But the heroic model of moral leadership usually doesn't work in the corporate world. Modesty and restraint are largely responsible for the achievements of the most effective moral leaders in business. The author, a specialist in business ethics, says the quiet leaders he has studied follow four basic rules in meeting ethical challenges and making decisions. The rules constitute an important resource for executives who want to encourage the development of such leaders among their middle managers. The first rule is "Put things off till tomorrow." The passage of time allows turbulent waters to calm and lets leaders' moral instincts emerge. "Pick your battles" means that quiet leaders don't waste political capital on fights they can't win; they save it for occasions when they really want to fight. "Bend the rules, don't break them" sounds easier than it is--bending the rules in order to resolve a complicated situation requires imagination, discipline, restraint, flexibility, and entrepreneurship. The fourth rule, "Find a compromise," reflects the author's finding that quiet leaders try not to see situations as polarized tests of ethical principles. These individuals work hard to craft compromises that are "good enough"--responsible and workable enough--to satisfy themselves, their companies, and their customers. The vast majority of difficult problems are solved through the consistent striving of people working far from the limelight. Their quiet approach to leadership doesn't inspire, thrill, or provide story lines for uplifting TV shows. But the unglamorous efforts of quiet leaders make a tremendous difference every day in the corporate world.

A motivational theory of life-span development. <=SEP=> This article had four goals. First, the authors identified a set of general challenges and questions that a life-span theory of development should address. Second, they presented a comprehensive account of their Motivational Theory of Life-Span Development. They integrated the model of optimization in primary and secondary control and the action-phase model of developmental regulation with their original life-span theory of control to present a comprehensive theory of development. Third, they reviewed the relevant empirical literature testing key propositions of the Motivational Theory of Life-Span Development. Finally, because the conceptual reach of their theory goes far beyond the current empirical base, they pointed out areas that deserve further and more focused empirical inquiry.

Chip and Skim: Cloning EMV Cards with the Pre-play Attack <=SEP=> EMV, also known as "Chip and PIN", is the leading system for card payments worldwide. It is used throughout Europe and much of Asia, and is starting to be introduced in North America too. Payment cards contain a chip so they can execute an authentication protocol. This protocol requires point-of-sale (POS) terminals or ATMs to generate a nonce, called the unpredictable number, for each transaction to ensure it is fresh. We have discovered two serious problems: a widespread implementation flaw and a deeper, more difficult to fix flaw with the EMV protocol itself. The first flaw is that some EMV implementers have merely used counters, timestamps or home-grown algorithms to supply this nonce. This exposes them to a "pre-play" attack which is indistinguishable from card cloning from the standpoint of the logs available to the card-issuing bank, and can be carried out even if it is impossible to clone a card physically. Card cloning is the very type of fraud that EMV was supposed to prevent. We describe how we detected the vulnerability, a survey methodology we developed to chart the scope of the weakness, evidence from ATM and terminal experiments in the field, and our implementation of proof-of-concept attacks. We found flaws in widely-used ATMs from the largest manufacturers. We can now explain at least some of the increasing number of frauds in which victims are refused refunds by banks which claim that EMV cards cannot be cloned and that a customer involved in a dispute must therefore be mistaken or complicit. The second problem was exposed by the above work. Independent of the random number quality, there is a protocol failure: the actual random number generated by the terminal can simply be replaced by one the attacker used earlier when capturing an authentication code from the card. This variant of the pre-play attack may be carried out by malware in an ATM or POS terminal, or by a man-in-the-middle between the terminal and the acquirer. We explore the design and implementation mistakes that enabled these flaws to evade detection until now: shortcomings of the EMV specification, of the EMV kernel certification process, of implementation testing, formal analysis, and monitoring customer complaints. Finally we discuss countermeasures. More than a year after our initial responsible disclosure of these flaws to the banks, action has only been taken to mitigate the first of them, while we have seen a likely case of the second in the wild, and the spread of ATM and POS malware is making it ever more of a threat.

A Simplified Agile Methodology for Ontology Development <=SEP=> In this paper we introduce SAMOD, a.k.a. Simplified Agile Methodology for Ontology Development, a novel agile methodology for the development of ontologies by means of small steps of an iterative workflow that focuses on creating well-developed and documented models starting from exemplar domain descriptions. RASH: https://w3id.org/people/essepuntato/papers/samod-owled2016.html

Does Inclusion of CIO in Top Management Team Impact Firm Performance? Evidence from a Long-Term Event Analysis <=SEP=> Inclusion of chief information officers (CIOs) in top management teams (TMTs) increases the functional and knowledge diversity of the teams, and elevates the role of IT in strategic business decisions. Considering that firms are increasingly relying on IT for operational efficiency and competitive advantage, TMTs with IT diversity can be expected to have superior performance compared to those without. While many studies have argued conceptually the importance of CIOs membership in TMTs, very few studies examined the performance effects of this membership. The goal of this study is to investigate this relationship. Moreover, we consider contingency factors that may have an effect on the focal relationship. Using firm-level secondary data and long-term event study methodology, we show that inclusion of CIO in TMT has a significant positive effect on firm performance, and this positive effect is larger for firms in dynamic environments and during the more recent years.

Optical Character Recognition <=SEP=> 72  Abstract— The Optical Character Recognition is a mobile application. It uses smart mobile phones of android platform. This paper combines the functionality of Optical Character Recognition and speech synthesizer. The objective is to develop user friendly application which performs image to speech conversion system using android phones. The OCR takes image as the input, gets text from that image and then converts it into speech. This system can be useful in various applications like banking, legal industry, other industries, and home and office automation. It mainly designed for people who are unable to read any type of text documents. In this paper, the character recognition method is presented by using OCR technology and android phone with higher quality camera.

AGE STANDARDIZATION OF RATES : A NEW WHO STANDARD <=SEP=> 2 Summary A recent WHO analysis has revealed the need for a new world standard population (see attached table). This has become particularly pertinent given the rapid and continued declines in age-specific mortality rates among the oldest old, and the increasing availability of epidemiological data for higher age groups. There is clearly no conceptual justification for choosing one standard over another, hence the choice is arbitrary. However, choosing a standard population with higher proportions in the younger age groups tends to weight events at these ages disproportionately. Similarly, choosing an older standard does the opposite. Hence, rather than selecting a standard to match the current age-structure of some population(s), the WHO adopted a standard based on the average age-structure of those populations to be compared (the world) over the likely period of time that a new standard will be used (some 25-30 years), using the latest UN assessment for 1998 (UN Population Division, 1998). From these estimates, an average world population age-structure was constructed for the period 2000-2025. The use of an average world population, as well as a time series of observations, removes the effects of historical events such as wars and famine on population age composition. The terminal age group in the new WHO standard population has been extended out to 100 years and over, rather than the 85 and over as is the current practice. The WHO World Standard population has fewer children and notably more adults aged 70 and above than the world standard. It is also notably younger than the European standard. It is important to note, however, that the age standardized death rates based on the new standard are not comparable to previous estimates that are based on some earlier standard(s). However, to facilitate comparative analyses, WHO will disseminate trend analyses of the " complete " historical mortality data using on the new WHO World Standard Population in future editions of the World Health Statistics Annual.

"Everybody knows what you're doing": a critical design approach to personal informatics <=SEP=> We present an alternative approach to the design of personal informatics systems: instead of motivating people to examine their own behaviors, this approach promotes awareness of and reflection on the infrastructures behind personal informatics and the modes of engagement that they promote. Specifically, this paper presents an interface that displays personal web browsing data. The interface aims to reveal underlying infrastructure using several methods: drawing attention to the scope of mined data by displaying deliberately selected sensitive data, using purposeful malfunction as a way to encourage reverse engineering, and challenging normative expectations around data mining by displaying information in unconventional ways. Qualitative results from a two-week deployment show that these strategies can raise people's awareness about data mining, promote efficacy and control over personal data, and inspire reflection on the goals and assumptions embedded in infrastructures for personal data analytics.

The neurophysiology of unmyelinated tactile afferents <=SEP=> CT (C tactile) afferents are a distinct type of unmyelinated, low-threshold mechanoreceptive units existing in the hairy but not glabrous skin of humans and other mammals. Evidence from patients lacking myelinated tactile afferents indicates that signaling in these fibers activate the insular cortex. Since this system is poor in encoding discriminative aspects of touch, but well-suited to encoding slow, gentle touch, CT fibers in hairy skin may be part of a system for processing pleasant and socially relevant aspects of touch. CT fiber activation may also have a role in pain inhibition. This review outlines the growing evidence for unique properties and pathways of CT afferents.

The nature of cyberbullying, and strategies for prevention <=SEP=> 0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.05.024 ⇑ Corresponding author. Address: Unit for School an University of London, UK. Tel.: +44 (0)207 919 7870; E-mail address: r.slonje@gmail.com (R. Slonje). Cyberbullying has been identified as an important problem amongst youth in the last decade. This paper reviews some recent findings and discusses general concepts within the area. The review covers definitional issues such as repetition and power imbalance, types of cyberbullying, age and gender differences, overlap with traditional bullying and sequence of events, differences between cyberbullying and traditional bullying, motives for and impact of cyber victimization, coping strategies, and prevention/intervention possibilities. These issues will be illustrated by reference to recent and current literature, and also by in-depth interviews with nine Swedish students aged 13–15 years, who had some first-hand experience of one or more cyberbullying episodes. We conclude by discussing the evidence for different coping, intervention and prevention strategies. 2012 Elsevier Ltd. All rights reserved.

Short Group Signatures <=SEP=> We construct a short group signature scheme. Signatures in our scheme are approximately the size of a standard RSA signature with the same security. Security of our group signature is based on the Strong Diffie-Hellman assumption and a new assumption in bilinear groups called the Decision Linear assumption. We prove security of our system, in the random oracle model, using a variant of the security definition for group signatures recently given by Bellare, Micciancio, and Warinschi.

A Deep Policy Inference Q-Network for Multi-Agent Systems <=SEP=> We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems—modeling agents with varying strategies—and propose to employ “policy features” learned from raw observations (e.g., raw images) of collaborators and opponents by inferring their policies. DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learningmodels. We further propose an enhanced version of DPIQN, called deep recurrent policy inference Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN are trained by an adaptive training procedure, which adjusts the network’s attention to learn the policy features and its own Q-values at different phases of the training process. We present a comprehensive analysis of DPIQN and DRPIQN, and highlight their effectiveness and generalizability in various multi-agent settings. Our models are evaluated in a classic soccer game involving both competitive and collaborative scenarios. Experimental results performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate superior performance to the baseline DQN and deep recurrent Q-network (DRQN) models. We also explore scenarios in which collaborators or opponents dynamically change their policies, and show that DPIQN and DRPIQN do lead to better overall performance in terms of stability and mean scores.

Four types of context for automatic spelling correction <=SEP=> This paper presents an investigation on using four types of contextual information for improving the accuracy of automatic correction of single-token non-word misspellings. The task is framed as contextually-informed re-ranking of correction candidates. Immediate local context is captured by word n-grams statistics from a Web-scale language model. The second approach measures how well a candidate correction fits in the semantic fabric of the local lexical neighborhood, using a very large Distributional Semantic Model. In the third approach, recognizing a misspelling as an instance of a recurring word can be useful for reranking. The fourth approach looks at context beyond the text itself. If the approximate topic can be known in advance, spelling correction can be biased towards the topic. Effectiveness of proposed methods is demonstrated with an annotated corpus of 3,000 student essays from international high-stakes English language assessments. The paper also describes an implemented system that achieves high accuracy on this task. RÉSUMÉ. Cet article présente une enquête sur l’utilisation de quatre types d’informations contextuelles pour améliorer la précision de la correction automatique de fautes d’orthographe de mots seuls. La tâche est présentée comme un reclassement contextuellement informé. Le contexte local immédiat, capturé par statistique de mot n-grammes est modélisé à partir d’un modèle de langage à l’échelle du Web. La deuxième méthode consiste à mesurer à quel point une correction s’inscrit dans le tissu sémantique local, en utilisant un très grand modèle sémantique distributionnel. La troisième approche reconnaissant une faute d’orthographe comme une instance d’un mot récurrent peut être utile pour le reclassement. La quatrième approche s’attache au contexte au-delà du texte lui-même. Si le sujet approximatif peut être connu à l’avance, la correction orthographique peut être biaisée par rapport au sujet. L’efficacité des méthodes proposées est démontrée avec un corpus annoté de 3 000 travaux d’étudiants des évaluations internationales de langue anglaise. Le document décrit également un système mis en place qui permet d’obtenir une grande précision sur cette tâche.

On the Power Leakage Problem in Beamspace MIMO Systems with Lens Antenna Array <=SEP=> The recently proposed concept of beamspace MIMO can significantly reduce the number of power- hungry radio frequency (RF) chains in millimeter- wave (mmWave) massive MIMO systems. However, most existing studies ignore the power leakage problem in beamspace MIMO systems, which results in an obvious loss in the achievable sum rate. In this paper, a phase shifter network (PSN)-based precoding structure is proposed to solve this problem. Its key idea is to employ multiple phase shifters from each RF chain to select multiple instead of only one beam to collect most of the leaked power. Based on the proposed structure, a rotation-based precoding algorithm is further designed to maximize the signal-to-noise-ratio (SNR) of each user by rotating the channel gains of the selected beams to the same direction. Simulation results show that the proposed PSN- based precoding can effectively collect the leaked power to achieve the near-optimal sum rate, and enjoys a higher energy efficiency than the conventional precoding solutions.

A Random Linear Network Coding Approach to Multicast <=SEP=> We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks

Insecticide Resistance Alleles Affect Vector Competence of Anopheles gambiae s.s. for Plasmodium falciparum Field Isolates <=SEP=> The widespread insecticide resistance raises concerns for vector control implementation and sustainability particularly for the control of the main vector of human malaria, Anopheles gambiae sensu stricto. However, the extent to which insecticide resistance mechanisms interfere with the development of the malignant malaria parasite in its vector and their impact on overall malaria transmission remains unknown. We explore the impact of insecticide resistance on the outcome of Plasmodium falciparum infection in its natural vector using three An. gambiae strains sharing a common genetic background, one susceptible to insecticides and two resistant, one homozygous for the ace-1(R) mutation and one for the kdr mutation. Experimental infections of the three strains were conducted in parallel with field isolates of P. falciparum from Burkina Faso (West Africa) by direct membrane feeding assays. Both insecticide resistant mutations influence the outcome of malaria infection by increasing the prevalence of infection. In contrast, the kdr resistant allele is associated with reduced parasite burden in infected individuals at the oocyst stage, when compared to the susceptible strain, while the ace-1 (R) resistant allele showing no such association. Thus insecticide resistance, which is particularly problematic for malaria control efforts, impacts vector competence towards P. falciparum and probably parasite transmission through increased sporozoite prevalence in kdr resistant mosquitoes. These results are of great concern for the epidemiology of malaria considering the widespread pyrethroid resistance currently observed in Sub-Saharan Africa and the efforts deployed to control the disease.

Dynamic time warping constraint learning for large margin nearest neighbor classification <=SEP=> Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method. ! 2011 Elsevier Inc. All rights reserved.

From the Internet of Computers to the Internet of Things <=SEP=> This paper1 discusses the vision, the challenges, possible usage scenarios and technological building blocks of the “Internet of Things”. In particular, we consider RFID and other important technological developments such as IP stacks and web servers for smart everyday objects. The paper concludes with a discussion of social and governance issues that are likely to arise as the vision of the Internet of Things becomes a reality.

Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning <=SEP=> In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks. We focus on evaluating transition function estimation, while we defer planning over this model to future work. Stochasticity is a fundamental property of many task environments. However, discriminative function approximators have difficulty estimating multimodal stochasticity. In contrast, deep generative models do capture complex high-dimensional outcome distributions. First we discuss why, amongst such models, conditional variational inference (VI) is theoretically most appealing for model-based RL. Subsequently, we compare different VI models on their ability to learn complex stochasticity on simulated functions, as well as on a typical RL gridworld with multimodal dynamics. Results show VI successfully predicts multimodal outcomes, but also robustly ignores these for deterministic parts of the transition dynamics. In summary, we show a robust method to learn multimodal transitions using function approximation, which is a key preliminary for model-based RL in stochastic domains.

Optimizing query execution for variable-aligned length compression of bitmap indices <=SEP=> Indexing is a fundamental mechanism for efficient data access. Recently, we proposed the Variable-Aligned Length (VAL) bitmap index encoding framework, which generalizes the commonly used word-aligned compression techniques. VAL presented a variable-aligned compression framework, which allows columns of a bitmap to be compressed using different encoding lengths. This flexibility creates a tunable compression that balances the trade-off between space and query processing time. The variable format of VAL presents several unique opportunities for query optimization.
 In this paper we explore multiple algorithms to optimize both point queries and range queries in VAL. In particular, we propose a dynamic encoding-length translation heuristic to process point queries. For range queries, we propose several column orderings based on the bitmap's metadata: largest segment length first (lsf), column size (size), and weighted size (ws). In our empirical study over both real and synthetic data sets, we show that our dynamic translation selection scheme produces query execution times only 3.5% below the optimal. We also found that the weighted size column ordering significantly and consistently out-performs other ordering techniques. Finally, we show that algorithms scale to data sets that are row-ordered.

Iris Recognition Based on Combined Feature of GLCM and Wavelet Transform <=SEP=> Iris biometric is considered to be one of the most efficient and trusted biometric method for authenticating users. This paper presents a method for iris recognition based on hybrid feature set of wavelet and GLCM features, a non-filter based technique, combined with Haar wavelet transform to increase the efficiency of the system. Here we combine frequency domain feature with spatial domain feature to increase overall efficiency of system. Probability neural Network is used to classify the features. Results show that the overall system efficiency is 94% with false rejection rate higher than false acceptance rate. The technique is tested on CASIA Iris database.

Modeling of rolling friction by recurrent neural network using LSTM <=SEP=> The modeling and identification of a mechanical system is the most important issue for many control systems in order to realize the desired control specifications. In particular, the friction characteristics often deteriorate the control performance, such as in the fast and precise positioning performance in industrial robots, the force estimation accuracy based on a disturbance observer, and the posture control performance of an inverted pendulum robot. Rolling friction tends to cause overshoot, undershoot, or limit cycles of the target value in positioning systems. In previous research, some model structures for rolling friction have been proposed to express the hysteresis characteristics in order to overcome these control issues. However, it is difficult to identify the correct parameters for precise modeling. In this paper, the modeling of rolling friction based on a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) is proposed to precisely express the rolling friction characteristics. The initial value design of the RNN during supervised learning is also presented to achieve a better model. The effectiveness of the proposed approach is verified by comparison with conventional friction models using an actual experimental setup.

Additive manufacturing of microwave components: Different approaches and methodologies <=SEP=> In this paper, two different approaches for the additive manufacturing of microwave component are discussed. The first approach consists is the popular and cheap fused deposition modeling (FDM). In this paper it is shown that, by using different infill factor, FDM is suitable for the manufacturing of devices with controlled dielectric constant. The second approach is the Stereolithography (SLA). With this approach, better results can be obtained in terms of resolution. Furthermore a very easy way to copper plate the surface of microwave devices is shown and it effectiveness is demonstrated through the manufacturing and measurement of a two-pole filter with mushroom-shaped resonators.