Query: NFC Loop Antenna in Conjunction With the Lower Section of a Metal Cover

Relevant passages:
NFC Antenna With Nonuniform Meandering Line and Partial Coverage Ferrite Sheet for Metal Cover Smartphone Applications <=SEP=> This paper proposes a near-field communication (NFC) antenna solution for metal-cover smartphone applications. In this NFC antenna solution, a narrow slot is initially loaded into the metal cover, and the position of this slot can be altered (with flexibility) according to the design of the smartphone’s external appearance. Next, an unconventional six-turn coil (with a six-sided irregular hexagonal shape) is designed that has a nonuniform linewidth and a nonuniform line gap between two lines, and it is partially loaded with a rectangular ferrite composite. In this design, an enhanced magnetic line of forces can be realized in certain specific locations, and an excellent inductively coupled near-field receiver is achieved. Notably, this proposed NFC antenna can pass the tests required by NFC forum certification, and its performances are comparable with the traditional NFC antenna that has nonmetallic cover.

Miniaturized NFC Antenna Design for a Tablet PC With a Narrow Border and Metal Back-Cover <=SEP=> A novel structure of the near-field communication (NFC) antenna design for a tablet PC is proposed. This tablet PC has a narrow border and full metallic back-cover. A miniaturized loop antenna design is achieved by attaching ferrite sheets on both sides of the loop antenna. The ferrite sheets may reduce eddy currents induced on the adjacent metallic back-cover by the loop antenna to improve the communication range of the NFC. Only the edge of the tablet PC allows the antenna to radiate due to the full metallic back-cover. Thus, the NFC antenna needs to be narrow to be installed on the edge of the tablet PC. Therefore, we propose a miniaturized NFC antenna with the dimensions of 41.5 (L) × 7.5 (W) × 0.45 (T) mm3 only. Simulated magnetic field distributions are consistent with measured voltage distributions. This design has a good communication range more than 6 cm in front of the touchscreen panel and reaches 2 cm over the other side above the metal back-cover.

A silver inkjet printed ferrite NFC antenna <=SEP=> This paper presents a near field communication (NFC) antenna that is printed directly on a ferrite substrate using a silver inkjet printing process. Such a silver inkjet printed ferrite NFC antenna provides a minimum substrate thickness, a good operation on metal objects, and a small assembly effort. The NFC antenna performance is analysed by measurements based on the ISO/IEC standard 10373-6 for proximity identification cards test methods. For this, the antenna is connected to an NFC microchip. Measurements in a non-metal environment show that the ferrite antenna performs equally good a custom-built NFC antenna printed on photo paper substrate, despite additional losses in the ferrite substrate. In a metal environment the ferrite antenna clearly outperforms the photo paper antenna.

NFC Antenna Design for Low-Permeability Ferromagnetic Material <=SEP=> A novel structure of a near-field communication (NFC) antenna for a mobile handset is proposed by changing the sequence of loop winding and modifying the inner loop structure. In general, the sintered ferrite sheets with higher relative permeability (μr ≈ 200) have been used to reduce the performance deterioration of NFC antennas due to the eddy current in the battery pack of a mobile handset. However, their costs are high, and they are considerably breakable. In this letter, we effectively enhance the performance of an NFC antenna by employing the ferrite-polymer composite with lower relative permeability (μr ≈ 55). The proposed antenna improves up to 23% more reading range and up to 65% better load modulation performance.

Material property of on-metal magnetic sheet attached on NFC/HF-RFID antenna and research of its proper pattern and size on <=SEP=> When a payment system with NFC/HF-RFID is installed in smart-phone, their communication with 13.56MHz is blocked by such metal as battery, coil and PCB. To solve this problem, magnetic sheet with high permeability is set between NFC/HF-RFID antenna and near-by metal. So far thin compound magnetic sheet or thin sintered ferrite has been used. In order to make thickness of these materials thinner, we propose amorphous magnetic sheet. Besides this amorphous magnetic sheet causes to increase the transmission effect.

On neural networks and learning systems for business computing <=SEP=> 

Going wireless: behavior & practice of new mobile phone users <=SEP=> We report on the results of a study in which 19 new mobile phone users were closely tracked for the first six weeks after service acquisition. Results show that new users tend to rapidly modify their perceptions of social appropriateness around mobile phone use, that actual nature of use frequently differs from what users initially predict, and that comprehension of service-oriented technologies can be problematic. We describe instances and features of mobile telephony practice. When in use, mobile phones occupy multiple social spaces simultaneously, spaces with norms that sometimes conflict: the physical space of the mobile phone user and the virtual space of the conversation.

Evolution of Architectural Floor Plans <=SEP=> Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. The process of layout planning has mostly been done by hand, with a handful of attempts to automate the process. This thesis explores some of these past attempts and describes several new techniques for automating the layout planning process using evolutionary computation. These techniques are inspired by the existing methods, while adding some of their own innovations. Additional experiments are done to test the possibility of allowing polygonal exteriors with rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design attempts. The system outlined in this thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.

Overview of the TREC 2017 Real-Time Summarization Track <=SEP=> Jimmy Lin, Adam Roegiest, Luchen Tan, Richard McCreadie, Ellen Voorhees, and Fernando Diaz 1 David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada 2 School of Computing Science, University of Glasgow, Scotland, the United Kingdom 3 National Institute for Standards and Technology, Maryland, USA 4 Microsoft Research, New York, USA {jimmylin, aroegies, luchen.tan}@uwaterloo.ca richard.mccreadie@glasgow.ac.uk, ellen.voorhees@nist.gov, fdiaz@microsoft.com

Human-comfortable navigation for an autonomous robotic wheelchair <=SEP=> Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

An ant system based on moderate search for TSP <=SEP=> Ant Colony Optimization (ACO) algorithms often suffer from criticism for the local optimum and premature convergence. In order to overcome these inherent shortcomings shared by most ACO algorithms, we divide the ordinary ants into two types: the utilizationoriented ants and the exploration-oriented ants. The utilization-oriented ants focus on constructing solutions based on the learned experience like ants in many other ACO algorithms. On the other hand, inspired by the adaptive behaviors of some real-world Monomorium ant species who tend to select paths with moderate pheromone concentration, a novel search strategy, that is, a completely new transition rule is designed for the exploration-oriented ants to explore more unknown solutions. In addition, a new corresponding update strategy is also employed. Moreover, applying the new search strategy and update strategy, we propose an improved version of ACO algorithm— Moderate Ant System. This improved algorithm is experimentally turned out to be effective and competitive.

A new ART-counterpropagation neural network for solving a forecasting problem <=SEP=> This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. q 2004 Elsevier Ltd. All rights reserved.

Narrowband Internet of Things: Evolutions, Technologies, and Open Issues <=SEP=> We are on the threshold of the explosive growth in the global Internet-of-Things (IoT) market. Comparing with the legacy human-centric applications, machine type communication scenarios exhibit totally different characteristics, such as low throughput, delay insensitivity, occasional transmission, and deep coverage. Meanwhile, it also requires the terminal devices to be cheap enough and sustain long battery life. These demands hasten the prosperity of low power wide area (LPWA) technologies. Narrowband IoT (NB-IoT) is the newest Long Term Evolution (LTE) specification ratified by the third generation partner project as one of the LPWA solutions to achieve the objectives of super coverage, low power, low cost, and massive connection. Working in the licensed frequency band, it is designed to reuse and coexist with the existing LTE cellular networks, which endows it with outstanding advantages in decreasing network installation cost and minimizing product time-to-market. In this backdrop, it has been extensively regarded as one of the most promising technologies toward the IoT landscape. However, as a new LTE standard, there are still a lot of challenges that need to overcome. This paper surveys its evolutions, technologies, and issues, spanning from performance analysis, design optimization, combination with other leading technologies, to implementation and application. The goal is to deliver a holistic understanding for the emerging wireless communication system, in helping to spur further research in accelerating the broad use of NB-IoT.

Search-Free DOD, DOA and Range Estimation for Bistatic FDA-MIMO Radar <=SEP=> Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramér-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.

Yoga for anxiety: a systematic review of the research evidence. <=SEP=> Between March and June 2004, a systematic review was carried out of the research evidence on the effectiveness of yoga for the treatment of anxiety and anxiety disorders. Eight studies were reviewed. They reported positive results, although there were many methodological inadequacies. Owing to the diversity of conditions treated and poor quality of most of the studies, it is not possible to say that yoga is effective in treating anxiety or anxiety disorders in general. However, there are encouraging results, particularly with obsessive compulsive disorder. Further well conducted research is necessary which may be most productive if focused on specific anxiety disorders.

A fully portable robot system for cleaning solar panels <=SEP=> Dust and dirt particles accumulating on PV panels decrease the solar energy reaching the cells, thereby reducing their overall power output. Hence, cleaning the PV panels is a problem of great practical engineering interest in solar PV power generation. In this paper, the problem is reviewed and methods for dust removal are discussed. A portable robotic cleaning device is developed and features a versatile platform which travels the entire length of a panel. An Arduino microcontroller is used to implement the robot's control system. Initial testing of the robot has provided favorable results and shows that such a system is viable. Future improvements on the design are discussed, especially the different methods of transporting the robot from one panel to another. In conclusion, it is found that robotic cleaning solution is practical and can help in maintaining the clean PV panel efficiency.

Induction and molecular signature of pathogenic TH17 cells <=SEP=> Interleukin 17 (IL-17)-producing helper T cells (TH17 cells) are often present at the sites of tissue inflammation in autoimmune diseases, which has led to the conclusion that TH17 cells are main drivers of autoimmune tissue injury. However, not all TH17 cells are pathogenic; in fact, TH17 cells generated with transforming growth factor-β1 (TGF-β1) and IL-6 produce IL-17 but do not readily induce autoimmune disease without further exposure to IL-23. Here we found that the production of TGF-β3 by developing TH17 cells was dependent on IL-23, which together with IL-6 induced very pathogenic TH17 cells. Moreover, TGF-β3-induced TH17 cells were functionally and molecularly distinct from TGF-β1-induced TH17 cells and had a molecular signature that defined pathogenic effector TH17 cells in autoimmune disease.

Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss <=SEP=> Monocular depth estimation benefits greatly from learning based techniques. By studying the training data, we observe that the per-pixel depth values in existing datasets typically exhibit a long-tailed distribution. However, most previous approaches treat all the regions in the training data equally regardless of the imbalanced depth distribution, which restricts the model performance particularly on distant depth regions. In this paper, we investigate the long tail property and delve deeper into the distant depth regions (i.e. the tail part) to propose an attentiondriven loss for the network supervision. In addition, to better leverage the semantic information for monocular depth estimation, we propose a synergy network to automatically learn the information sharing strategies between the two tasks. With the proposed attention-driven loss and synergy network, the depth estimation and semantic labeling tasks can be mutually improved. Experiments on the challenging indoor dataset show that the proposed approach achieves state-of-the-art performance on both monocular depth estimation and semantic labeling tasks.

Nutrition Therapy Recommendations for the Management of Adults With
Diabetes <=SEP=> There is no standard meal plan or eating pattern that works universally for all people with diabetes. In order to be effective, nutrition therapy should be individualized for each patient/client based on his or her individual health goals; personal and cultural preferences; health literacy and numeracy; access to healthful choices; and readiness, willingness, and ability to change. Nutrition interventions should emphasize a variety of minimally processed nutrient dense foods in appropriate portion sizes as part of a healthful eating pattern and provide the individual with diabetes with practical tools for day-to-day food plan and behavior change that can be maintained over the long term.

Impact Of Online Advertising On Consumer Attitudes And Interests Buy Online ( Survey On Students Of Internet Users In Makassar ) <=SEP=> The rapid development of technology today makes Internet users continues to increase. This is supported by the ease of internet users access the internet either through a PC, laptop, mobile phones, tablets and other media. The increase in Internet users this makes the internet into a proper promotion using online advertising. With a wide reach and touch the various layers of the Internet media communities may be appropriate advice for company promotion. However, an increasing number of Internet users, especially in the city of Makassar is not accompanied by an increase in the number of online purchases. Based on that it is necessary to examine how the effect of online advertising on consumer buying behavior and online, as well as how to control the behavior and subjective norms influence the attitudes and interests of consumers buy online. This study aims to analyze and test the effect of online advertising on consumer attitudes and purchase interest online, a survey conducted on students of Internet users in the city of Makassar. The study was conducted on students of public and private universities in the city of Makassar. The method used was a quantitative analysis using the technique of purposive sampling method with a sample of 340 people. Testing this hypothesis using structural equation modeling (SEM). The results showed that online advertising has an influence on consumer buying behavior and online. Dimensions interactivity of online advertising provides the highest influence on the attitudes and interests of consumers purchasing online.

Bernoulli Embeddings for Graphs <=SEP=> Just as semantic hashing (Salakhutdinov and Hinton 2009) can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be applied to the approximate expected loss. Embeddings optimized in this fashion consistently outperform the quantization of both spectral graph embeddings and various learned real-valued embeddings, on both ranking and pre-ranking tasks for a variety of datasets.

Playful bottle: a mobile social persuasion system to motivate healthy water intake <=SEP=> This study of mobile persuasion system explores the use of a mobile phone, when attached to an everyday object used by an everyday behavior, becomes a tool to sense and influence that behavior. This mobile persuasion system, called Playful Bottle system, makes use of a mobile phone attached to an everyday drinking mug and motivates office workers to drink healthy quantities of water. A camera and accelerometer sensors in the phone are used to build a vision/motion-based water intake tracker to detect the amount and regularity of water consumed by the user. Additionally, the phone includes hydration games in which natural drinking actions are used as game input. Two hydration games are developed: a single-user TreeGame with automated computer reminders and a multi-user ForestGame with computer-mediated social reminders from members of the group playing the game. Results from 7-week user study with 16 test subjects suggest that both hydration games are effective for encouraging adequate and regular water intake by users. Additionally, results of this study suggest that adding social reminders to the hydration game is more effective than system reminders alone.

Working memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD): an examination of central executive and storage/rehearsal processes. <=SEP=> The current study was the first to use a regression approach to examine the unique contributions of central executive (CE) and storage/rehearsal processes to working memory (WM) deficits in adults with ADHD. Thirty-seven adults (ADHD = 21, HC = 16) completed phonological (PH) and visuospatial (VS) working memory tasks. While both groups performed significantly better during the PH task relative to the VS task, adults with ADHD exhibited significant deficits across both working memory modalities. Further, the ADHD group recalled disproportionately fewer PH and VS stimuli as set-size demands increased. Overall, the CE and PH storage/rehearsal processes of adults with ADHD were both significantly impaired relative to those of the healthy control adults; however, the magnitude of the CE effect size was much smaller compared to previous studies of children with the disorder. Collectively, results provide support for a lifelong trajectory of WM deficits in ADHD.

Design of Organic TFT Pixel Electrode Circuit for Active-Matrix OLED Displays <=SEP=> A new current-programming pixel circuit for active-matrix organic light-emitting diode (AM-OLED) displays, composed of four organic thin-film transistors (OTFTs) and one capacitor, has been designed, simulated and evaluated. The most critical issue in realizing AMOLED displays with OTFTs is the variation and aging of the driving-TFTs and degradation of OLEDs. This can cause image sticking or degradation of image quality. These problems require compensation methods for high-quality display applications, and pixel level approach is considered to be one of the most important factors for improving display image quality. Our design shows that the current OLED and OTFT technology can be implemented for AMOLED displays, compensating the degradation of OTFT device characteristics.

Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning <=SEP=> As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning. Therefore, a key issue to solve in this area is how to effectively model the multi-scale correspondence structure properties in an adaptive end-to-end learning fashion. Motivated by this observation, we propose an endto-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation. In principle, the proposed MSCSL approach is capable of effectively capturing the multi-scale inter-image-correlation correspondence structures within a multi-level feature space from deep learning. Moreover, the proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures. Finally, the above procedures for correspondence structure learning and multi-scale dependency modeling are implemented in a unified end-to-end deep learning framework. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach.

Unsupervised Pattern Discovery in Speech <=SEP=> We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a prespecified inventory of lexical units (i.e., phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multiword phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.

Perspectives and Visions of Computer Science Education in Primary and Secondary (K-12) Schools <=SEP=> In view of the recent developments in many countries, for example, in the USA and in the UK, it appears that computer science education (CSE) in primary or secondary schools (K-12) has reached a significant turning point, shifting its focus from ICT-oriented to rigorous computer science concepts. The goal of this special issue is to offer a publication platform for soundly based in-depth experiences that have been made around the world with concepts, approaches, or initiatives that aim at supporting this shift. For this purpose, the article format was kept as large as possible, enabling the authors to explain many facets of their concepts and experiences in detail. Regarding the structure of the articles, we had encouraged the authors to lean on the Darmstadt Model, a category system that was developed to support the development, improvement, and investigation of K-12 CSE across regional or national boundaries. This model could serve as a unifying framework that might provide a proper structure for a well-founded critical discussion about the future of K-12 CSE. Curriculum designers or policy stakeholders, who have to decide, which approach an upcoming national initiative should follow, could benefit from this discussion as well as researchers who are investigating K12 CSE in any regard. With this goal in mind, we have selected six extensive and two short case studies from the UK, New Zealand, USA/Israel, France, Sweden, Georgia (USA), Russia, and Italy that provide an in-depth analysis of K-12 CSE in their respective country or state.

Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations <=SEP=> Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera setup and the 3D structure of the human body to probabilistically combine per view 2D ConvNet predictions into a globally optimal 3D pose. This 3D pose is used as the basis for harvesting annotations. The benefit of the annotations produced automatically with our approach is demonstrated in two challenging settings: (i) fine-tuning a generic ConvNet-based 2D pose predictor to capture the discriminative aspects of a subjects appearance (i.e.,personalization), and (ii) training a ConvNet from scratch for single view 3D human pose prediction without leveraging 3D pose groundtruth. The proposed multi-view pose estimator achieves state-of-the-art results on standard benchmarks, demonstrating the effectiveness of our method in exploiting the available multi-view information.

Design and development of a mobile crawling robot with novel halbach array based magnetic wheels <=SEP=> Higher efficiency and the safety of hull inspection can be achieved by replacing the conventional methods with modern crawling robots. This paper consists of detailed study of the design and development of a crawling robot with a novel permanent magnetic wheel based on Halbach magnetic array. The magnetic array and the wheel assembly were designed based on the magnetic simulation results. Maintaining the adequate adhesion force as well as the friction force of the wheels were considered in designing the wheels. The crawling robot is equipped with a steering system based on Ackermann steering method hence the steering angles are controlled by an algorithm and actuated using servo motors. The driving system of the robot is equipped with four geared stepper motors and they are controlled separately by the algorithm according to the inputs of the controller. The control architecture and the components used for the robot control system are explained. The operator can remotely control the robot and inspect the hull surface via the camera feed available in the HMI (Human Machine Interface) of the robot. The prototype of the crawling robot was tested in a similar environment and the test results are included in the paper.

Security as a Service Model for Cloud Environment <=SEP=> Cloud computing is becoming increasingly important for provision of services and storage of data in the Internet. However there are several significant challenges in securing cloud infrastructures from different types of attacks. The focus of this paper is on the security services that a cloud provider can offer as part of its infrastructure to its customers (tenants) to counteract these attacks. Our main contribution is a security architecture that provides a flexible security as a service model that a cloud provider can offer to its tenants and customers of its tenants. Our security as a service model while offering a baseline security to the provider to protect its own cloud infrastructure also provides flexibility to tenants to have additional security functionalities that suit their security requirements. The paper describes the design of the security architecture and discusses how different types of attacks are counteracted by the proposed architecture. We have implemented the security architecture and the paper discusses analysis and performance evaluation results.