Query: Miniaturized Circularly Polarized Patch Antenna With Low Back Radiation for GPS Satellite Communications

Relevant passages:
A Miniaturized, Dual-Band, Circularly Polarized Microstrip Antenna for Installation Into Satellite Mobile Phones <=SEP=> A miniaturized, dual-band, circularly polarized microstrip antenna (CPMA) for satellite mobile phones was designed. Two miniaturized radiation elements using folded structures are stacked vertically to achieve dual bands. Each radiation element shows left-handed circular polarization. The total volume of this dual-band, circularly polarized, microstrip antenna is 46 mm (length) x 24 mm (width) x 13 mm (height). The overall antenna is mounted on a phone-shaped ground plane to fit inside satellite mobile phones.

Design of a Compact GPS and SDARS Integrated Antenna for Automotive Applications <=SEP=> This letter describes a novel compact solution for integrating a Global Positioning System (GPS) and a Satellite Digital Audio Radio Service (SDARS) antenna in a very small volume to satisfy the requirements of the automotive market. The GPS antenna is a classic small commercial ceramic patch, and the SDARS antenna is designed in order to fit in the reduced space without affecting radiation and band performance of the GPS antenna. The SDARS basic geometry is a square-ring microstrip antenna operating in the frequency range 2.320-2.345 GHz with left-hand circular polarization (LHCP), and it is wrapped around the GPS patch, which receives a right-hand circular polarization (RHCP) signal at 1.575 GHz. The overall volume of the presented integrated antenna solution is only 30 x 30 x 7.6 mm3, rendering it attractive for the automotive market.

A DUAL BAND CIRCULARLY POLARIZED RING ANTENNA BASED ON COMPOSITE RIGHT AND LEFT HANDED METAMATERIALS <=SEP=> Abstract—In this paper a dual band circularly polarized antenna is designed, fabricated, and measured. Based on the concept of composite right and left handed (CRLH) metamaterials, the same phase constants and current distributions on the ring antenna are achieved at two frequency bands. Thus, the antenna has similar radiation patterns at both bands. The circular polarization is implemented by feeding two vertical ports from power dividers that provide equal magnitudes and quadrature phase excitations.

Compact Circularly-Polarized Patch Antenna Loaded With Metamaterial Structures <=SEP=> A metamaterial-inspired low-profile patch antenna is proposed and studied for circularly-polarized (CP) radiation. The present antenna, which has a single-fed configuration, is loaded with the composite right/left-handed (CRLH) mushroom-like structures and a reactive impedance surface (RIS) for miniaturization purpose. The CP radiation is realized by exciting two orthogonally-polarized modes simultaneously which are located in the left-handed (LH) region. The detailed antenna radiation characteristics are examined and illustrated with both simulated and experimental results. The CP performance can be controlled in several different ways. This antenna exhibits an overall size of 0.177λ0 × 0.181λ0 × 0.025λ0 at 2.58 GHz and a radiation efficiency around 72%. Finally, based on the proposed CP patch antenna, a compact dual-band dual linearly-polarized patch antenna has also been designed and fabricated. Promising experimental results are observed.

Omnidirectional Circularly Polarized Antenna Utilizing Zeroth-Order Resonance of Epsilon Negative Transmission Line <=SEP=> The omnidirectional circularly polarized (CP) antenna using a circular mushroom structure with curved branches is proposed. The antenna is based on the zeroth-order resonance (ZOR) mode of epsilon negative (ENG) transmission line (TL) to obtain a vertical polarization and an omnidirectional radiation pattern. Also, the horizontal polarization is obtained by the curved branches. The 90° phase difference between two orthogonal polarizations is inherently provided by the zeroth-order resonator. Therefore, the antenna has an omnidirectional CP radiation pattern in the azimuthal plane. In addition, this antenna is planar type and simply designed without a dual feeding structure and 90° phase shifter. The measured average axial ratio and left-hand (LH) CP gain are 2.03 dB and - 0.40 dBic, respectively, in the azimuthal plane.

Gaze Direction based Mobile Application for Quadriplegia Wheelchair Control System <=SEP=> People with quadriplegia recruit the interest of researchers in introducing automated movement systems for adopted special purpose wheelchairs. These systems were introduced for easing the movement of such type of disabled people independently. This paper proposed a comprehensive control system that can control the movement of Quadriplegia wheelchairs using gaze direction and blink detection. The presented system includes two main parts. The first part consists of a smartphone that applies the propose gaze direction detection based mobile application. It then sends the direction commands to the second part via Wi-Fi connection. The second part is a prototype representing the quadriplegia wheelchair that contains robotic car (two-wheel driving car), Raspberry Pi III and ultrasound sensors. The gaze direction commands, sent from the smartphone, are received by the Raspberry Pi for processing and producing the control signals. The ultrasound sensors are fixed at the front and back of the car for performing the emergency stop when obstacles are detected. The proposed system is based on gaze tracking and direction detection without the requirement of calibration with additional sensors and instruments. The obtained results show the superior performance of the proposed system that proves the claim of authors. The accuracy ratio is ranged between 66% and 82% depending on the environment (indoor and outdoor) and surrounding lighting as well as the smart phone type. Keywords—Gaze direction detection; mobile application; obstacle detection; quadriplegia; Raspberry Pi microcomputer

Understanding crowdfunding work: implications for support tools <=SEP=> Crowdfunding is changing the way people realize their work by providing a new way to gain support from a distributed audience. This study seeks to understand the work of crowdfunding project creators in order to inform the design of crowdfunding support tools and systems. We conducted interviews with 30 project creators from three popular crowdfunding platforms in order to understand what tasks are involved and what tools people use to accomplish crowdfunding work. Initial results suggest that project creators carry out three main types of work-preparing the campaign material, marketing the project, and following through with project goals-and have adapted general support tools to facilitate doing this work. From our initial findings, we hope to improve and design future crowdfunding support tools and systems.

Delicate manipulations with compliant mechanism and electrostatic adhesion <=SEP=> Traditional rigid robotic hand manipulator has been used in many field nowadays due to its advantages of large gripping force and stable performance. However, this kind of rigid manipulator is not suitable for gripping fragile objects since it is motorized and force control can be a problem. It is also not suitable to grip object with different shapes since the manipulator is rigid and not compliant. In this study, a novel manipulator with gripping capability is designed and fabricated. The manipulator combines electrostatic adhesion actuation with soft manipulators. The manipulator has high flexibility and can be compliant to different shapes due to the property of the materials. It is very promising to do delicate manipulations in industry field and biomedical field.

A hybrid approach for database intrusion detection at transaction and inter-transaction levels <=SEP=> Nowadays, information plays an important role in the organizations. Sensitive information is often stored within the database. Traditional mechanisms such as encryption, access control, and authentication cannot provide a high level of confidence. Therefore, the existence of Intrusion Detection Systems in the database is a necessity. In this paper, we propose a type of intrusion detection system for detecting attacks in both database transaction level and inter-transaction level (user task level). For this purpose, we propose a detection method at transaction level, which is based on describing the expected transactions within the database applications. Then at inter-transaction level, we propose a detection method that is based on anomaly detection and uses data mining to find dependency and sequence rules. The advantage of this system compared to the previous database intrusion detection systems is that it can detect malicious behaviors in both transaction and inter-transaction levels. Also, it gains advantages of a hybrid method, including specification-based detection and anomaly detection, to minimize both false positive and false negative errors. In order to evaluate the accuracy of the proposed system, some experiments have been done. The experimental evaluation results show high accuracy and effectiveness of the proposed system.

Deep Domain Adaptation in Action Space <=SEP=> In the general settings of supervised learning, human action recognition has been a widely studied topic. The classifiers learned in this setting assume that the training and test data have been sampled from the same underlying probability distribution. However, in most of the practical scenarios, this assumption is not true, resulting in a suboptimal performance of the classifiers. This problem, referred to as Domain Shift, has been extensively studied, but mostly for image/object classification task. In this paper, we investigate the problem of Domain Shift in action videos, an area that has remained under-explored, and propose two new approaches named Action Modeling on Latent Subspace (AMLS) and Deep Adversarial Action Adaptation (DAAA). In the AMLS approach, the action videos in the target domain are modeled as a sequence of points on a latent subspace and adaptive kernels are successively learned between the source domain point and the sequence of target domain points on the manifold. In the DAAA approach, an end-to-end adversarial learning framework is proposed to align the two domains. The action adaptation experiments were conducted using various combinations of multi-domain action datasets, including six common classes of Olympic Sports and UCF50 datasets and all classes of KTH, MSR and our own SonyCam datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.

Parallel Computer Architecture: A Hardware/Software Approach <=SEP=> enter the circuit several times, possibly via different input processors. More elaborate combinatorial considerations than for standard VLSI circuits are needed to show how communication complexity can be used to get lower bounds on multilective VLSI computations. Near the end of the book, Hromkovic presents relations between communication complexity and some complexity measures of sequential computations. The core section introduces a uniform model of one-way communication protocols and shows that the corresponding uniform one-way communication complexity is strongly related to the size of deterministic finite automata. Then he relates communication complexity to the time and space complexity of Turing machines. Finally, he shows how communication complexity can be used to obtain lower bounds on the size and the depth of decisions nees and branching programs.

A Taxi Order Dispatch Model based On Combinatorial Optimization <=SEP=> Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.
 We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.

Sub-Nyquist Radar via Doppler Focusing <=SEP=> We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here, we describe a sub-Nyquist sampling and recovery approach called Doppler focusing, which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size, which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys a signal-to-noise ratio (SNR) improvement, which scales linearly with P, obtaining good detection performance even at SNR as low as - 25 dB. The recovery is based on the Xampling framework, which allows reduction of the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling radar prototype.

The diffusion of misinformation on social media: Temporal pattern, message, and source <=SEP=> This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temp oral attern, content mutation, and sources of misinformation. We traced the lifecycle of 17 po pular political rumors that circulated on Twitter over 13 months during the 2012 U.S. preside ntial election. Using text analysis based on time series, we found that while false rumors (misi nformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension aro und the target dissolves. We observed that rumors resurface by partisan news websites that rep ckage the old rumor into news and, gain visibility by influential Twitter users who introdu ce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mecha nism by which these messages evolve over time.

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

End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection <=SEP=> Object detection methods usually represent objects through rectangular bounding boxes from which they extract features, regardless of their actual shapes. In this paper, we apply deformations to regions in order to learn representations better fitted to objects. We introduce DP-FCN, a deep model implementing this idea by learning to align parts to discriminative elements of objects in a latent way, i.e. without part annotation. This approach has two main assets: it builds invariance to local transformations, thus improving recognition, and brings geometric information to describe objects more finely, leading to a more accurate localization. We further develop both features in a new model named DP-FCN2.0 by explicitly learning interactions between parts. Alignment is done with an in-network joint optimization of all parts based on a CRF with custom potentials, and deformations are influencing localization through a bilinear product. We validate our models on PASCAL VOC and MS COCO datasets and show significant gains. DP-FCN2.0 achieves state-of-the-art results of 83.3 and 81.2% on VOC 2007 and 2012 with VOC data only.

Suicidal strangulation by double ligature: a case report. <=SEP=> This article reports a self-strangulation suicide in which a double complex group of ligatures was used. Although similar cases have been referenced in forensic literature, this case is notable because of the unusual method used by the victim. A 61-year-old man was found in a locked room with a double elaborate ligature comprising six wire clothes hangers completely encircling the neck and a black rubber band in a double loop. Autopsy also documented parallel superficial cut lesions in proximal forearm interpreted as hesitation marks. It stresses the importance of characteristics such as analysis of number of ligatures, position of the knots, number of knots and turns of ligature marks and the absence of any defense and relevant internal injuries inherent to suicide ligature strangulation cases.

An Evaluation of Distributed Concurrency Control <=SEP=> Increasing transaction volumes have led to a resurgence of interest in distributed transaction processing. In particular, partitioning data across several servers can improve throughput by allowing servers to process transactions in parallel. But executing transactions across servers limits the scalability and performance of these systems. In this paper, we quantify the effects of distribution on concurrency control protocols in a distributed environment. We evaluate six classic and modern protocols in an in-memory distributed database evaluation framework called Deneva, providing an apples-to-apples comparison between each. Our results expose severe limitations of distributed transaction processing engines. Moreover, in our analysis, we identify several protocol-specific scalability bottlenecks. We conclude that to achieve truly scalable operation, distributed concurrency control solutions must seek a tighter coupling with either novel network hardware (in the local area) or applications (via data modeling and semantically-aware execution), or both.

Hierarchical Self-Organization in Genetic programming <=SEP=> This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation onthe-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibilityof approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem’s fitness function. Preliminary empirical results are presented.

An Introduction to Variational Methods for Graphical Models <=SEP=> This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.

Contactless DC Connector Based on GaN LLC Converter for Next-Generation Data Centers <=SEP=> An inductively coupled contactless dc connector has been proposed for the next-generation 380-V dc distribution system in data centers. A LLC resonant dc-dc converter topology with gallium nitride (GaN) power transistors has been applied to realize the short-distance highly efficient contactless power transfer. A prototype of a 1.2-kW 384- to 192-V connector has been fabricated and the conversion efficiency of over 95% with the power density of 8.1 W/cm3 has been confirmed experimentally under 1000-kHz operation. The design consideration has been carried out and the potential to achieve 10.0 W/cm3 has been also shown taking the feature of the GaN power device and the characteristics of the magnetic core material for the transformer into account. The contactless dc connector integrates the functioning of an isolated dc-dc converter into a connector for space saving, and the dc current can be cut off without arc because of the inductive coupling. The proposed connector contributes to realizing a highly efficient, space saving, and reliable future 380-V dc distribution system.

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.

Deep Sentiment Representation Based on CNN and LSTM <=SEP=> Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-term memory recurrent neural network (LSTM) is proposed. The model uses the pre-trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).

GQ ( λ ) : A general gradient algorithm for temporal-difference prediction learning with eligibility traces <=SEP=> A new family of gradient temporal-difference learning algorithms have recently been introduced by Sutton, Maei and others in which function approximation is much more straightforward. In this paper, we introduce the GQ(λ) algorithm which can be seen as extension of that work to a more general setting including eligibility traces and off-policy learning of temporally abstract predictions. These extensions bring us closer to the ultimate goal of this work—the development of a universal prediction learning algorithm suitable for learning experientially grounded knowledge of the world. Eligibility traces are essential to this goal because they bridge the temporal gaps in cause and effect when experience is processed at a temporally fine resolution. Temporally abstract predictions are also essential as the means for representing abstract, higher-level knowledge about courses of action, or options. GQ(λ) can be thought of as an extension of Q-learning. We extend existing convergence results for policy evaluation to this setting and carry out a forward-view/backwardview analysis to derive and prove the validity of the new algorithm. Introduction One of the main challenges in artificial intelligence (AI) is to connect the low-level experience to high-level representations (grounded world knowledge). Low-level experience refers to rich signals received back and forth between the agent and the world. Recent theoretical developments in temporal-difference learning combined with mathematical ideas developed for temporally abstract options, known as intra-option learning, can be used to address this challenge (Sutton, 2009). Intra-option learning (Sutton, Precup, and Singh, 1998) is seen as a potential method for temporalabstraction in reinforcement learning. Intra-option learning is a type of off-policy learning. Off-policy learning refers to learning about a target policy while following another policy, known as behavior policy. Offpolicy learning arises in Q-learning where the target policy is a greedy optimal policy while the behavior policy is exploratory. It is also needed for intra-option learning. Intra-option methods look inside options and allow AI agent to learn about multiple different options simultaneously from a single stream of received data. Option refers to a temporally course of actions with a termination condition. Options are ubiquitous in our everyday life. For example, to go for hiking, we need to consider and evaluate multiple options such as transportation options to the hiking trail. Each option includes a course of primitive actions and only is excited in particular states. The main feature of intra-option learning is its ability to predict the consequences of each option policy without executing it while data is received from a different policy. Temporal difference (TD) methods in reinforcement learning are considered as powerful techniques for prediction problems. In this paper, we consider predictions always in the form of answers to the questions. Questions are like “If of follow this trail, would I see a creek?” The answers to such questions are in the form of a single scalar (value function) that tells us about the expected future consequences given the current state. In general, due to the large number of states, it is not feasible to compute the exact value of each state entry. One of the key features of TD methods is their ability to generalize predictions to states that may not have visited; this is known as function approximation. Recently, Sutton et al. (2009b) and Maei et al. (2009) introduced a new family of gradient TD methods in which function approximation is much more straightforward than conventional methods. Prior to their work, the existing classical TD algorithms (e.g.; TD(λ) and Q-learning) with function approximation could become unstable and diverge (Baird, 1995; Tsitsiklis and Van Roy, 1997). In this paper, we extend their work to a more general setting that includes off-policy learning of temporally abstract predictions and eligibility traces. Temporally abstract predictions are essential for representing higher-level knowledge about the course of actions, or options (Sutton et al., 1998). Eligibility traces bridge between the temporal gaps when experience is processes at a temporally fine resolution. In this paper, we introduce the GQ(λ) algorithm that can be thought of as an extension to Q-learning (Watkins and Dayan, 1989); one of the most popular off-policy learning algorithms in reinforcement learning. Our algorithm incorporates gradient-descent ideas originally developed by Sutton et al. (2009a,b), for option conditional predictions with varying eligibility traces. We extend existing convergence results for policy evaluation to this setting and carry forward-view/backwardview analysis and prove the validity of the new algorithm. The organization of the paper is as follows: First, we describe the problem setting and define our notations. Then we introduce the GQ(λ) algorithm and describe how to use it. In the next sections we provide derivation of the algorithm and carry out analytical analysis on the equivalence of TD forward-view/backward-view. We finish the paper with convergence proof and conclusion section. Notation and background We consider the problem of policy evaluation in finite state-action Markov Decision Process (MDP). Under standard conditions, however, our results can be extended to MDPs with infinite state–action pairs. We use a standard reinforcement learning (RL) framework. In this setting, data is obtained from a continually evolving MDP with states st ∈ S, actions at ∈ A, and rewards rt ∈ <, for t = 1, 2, . . ., with each state and reward as a function of the preceding state and action. Actions are chosen according to the behavior policy b, which is assumed fixed and exciting, b(s, a) > 0,∀s, a. We consider the transition probabilities between state– action pairs, and for simplicity we assume there is a finite number N of state–action pairs. Suppose the agent find itself at time t in a state– action pair st, at. The agent likes its answer at that time to tell something about the future sequence st+1, at+1, . . . , st+k if actions from t + 1 on were taken according to the option until it terminated at time t+k. The option policy is denoted π : S × A → [0, 1] and whose termination condition is denoted β : S → [0, 1]. The answer is always in the form of a single number, and of course we have to be more specific about what we are trying to predict. There are two common cases: 1) we are trying to predict the outcome of the option; we want to know about the expected value of some function of the state at the time the option terminates. We call this function the outcome target function, and denote it z : S → <, 2) we are trying to predict the transient; that is, what happens during the option rather than its end. The most common thing to predict about the transient is the total or discounted reward during the option. We denote the reward function r : S ×A → <. Finally, the answer could conceivably be a mixture of both a transient and an outcome. Here we will present the algorithm for answering questions with both an outcome part z and a transient part r, with the two added together. In the common place where one wants only one of the two, the other is set to zero. Now we can start to state the goal of learning more precisely. In particular, we would like our answer to be equal to the expected value of the outcome target function at termination plus the cumulative sum of the transient reward function along the way: Q(st, at) (1) ≡ E [ rt+1 + γrt+2 + · · ·+ γrt+k + zt+k | π, β ] , where γ ∈ (0, 1] is discount factor and Q(s, a) denotes action value function that evaluates policy π given state-action pair s, a. To simplify the notation, from now on, we drop the superscript π on action values. In many problems the number of state-action pairs is large and therefore it is not feasible to compute the action values for each state-action entry. Therefore, we need to approximate the action values through generalization techniques. Here, we use linear function approximation; that is, the answer to a question is always formed linearly as Qθ(s, a) = θ>φ(s, a) ≈ Q(s, a) for all s ∈ S and a ∈ A, where θ ∈ < is a learned weight vector and φ(s, a) ∈ < indicates a state–action feature vector. The goal is to learn parameter vector θ through a learning method such as TD learning. The above (1) describes the target in a Monte Carlo sense, but of course we want to include the possibility of temporal-difference learning; one of the widely used techniques in reinforcement learning. To do this, we provide an eligibility-trace function λ : S → [0, 1] as described in Sutton and Barto (1998). We let eligibilitytrace function, λ, to vary over different states. In the next section, first we introduce GQ(λ); a general temporal-difference learning algorithm that is stable under off-policy training, and show how to use it. Then in later sections we provide the derivation of the algorithm and convergence proof. The GQ(λ) algorithm In this section we introduce the GQ(λ) algorithm for off-policy learning about the outcomes and transients of options, in other words, intra-option GQ(λ) for learning the answer to a question chosen from a wide (possibly universal) class of option-conditional predictive questions. To specify the question one provides four functions: π and β, for the option, and z and r, for the target functions. To specify how the answers will be formed one provides their functional form (here in linear form), the feature vectors φ(s, a) for all state–action pairs, and the eligibility-trace function λ. The discount factor γ can be taken to be 1, and thus ignored; the same effect as discounting can be achieved through the choice of β. Now, we specify the GQ(λ) algorithm as follows: The weight vector θ ∈ < is initialized arbitrarily. The secondary weight vector w ∈ < is init

What Do Customers Crave in Mobile 5G?: A survey spotlights four standout factors. <=SEP=> Increasing customer demand coupled with limitations in current fourth-generation (4G) wireless mobile networks and advances toward the next-generation wireless mobile network have all together created a need for us to look at what customers today expect from the upcoming fifth-generation (5G) network. This study was conducted among existing 4G users in the United States, the United Kingdom, Singapore, and Australia. The research was completed in two parts: qualitative and quantitative. The quantitative part was accomplished by performing data collection and selection through surveys and data evaluation by structural equation modeling (SEM) using SMARTPLS.

Multi-target tracking scheme using a track management table for automotive radar systems <=SEP=> In this paper, a multiple target tracking scheme for automotive radars is presented. First, a track table for efficient track management is proposed; then, a track management function is designed using a state machine. The proposed scheme can initiate, confirm, and delete all tracks of multiple targets. Moreover, it can determine whether the detected target is a real target or a ghost track, and a terminated track or a sudden missing track. The simulation results demonstrate that the proposed tracking scheme is processed normally.

Technochange management: using IT to drive organizational change <=SEP=> Using IT in ways that can trigger major organizational changes creates high-risk, potentially high-reward, situations that I call technochange (for technology-driven organizational change). Technochange differs from typical IT projects and from typical organizational change programs and therefore requires a different approach. One major risk in technochange—that people will not use information technology and related work practices—is not thoroughly addressed by the discipline of IT project management, which focuses on project cost, project schedule, and solution functionality. Organizational change management approaches are also generally not effective on their own, because they take as a given the IT ‘‘solutions’’ developed by a technical team. Consequently, the potential for the IT ‘‘solution’’ to be misaligned with important organizational characteristics, such as culture or incentives, is great. Merely combining IT project management and organizational change management approaches does not produce the best results, for two reasons. First, the additive approach does not effectively address the many failure-threatening problems that can arise over the lengthy sequential process of the typical technochange lifecycle. Second, the additive approach is not structured to produce the characteristics of a good technochange solution: a complete intervention consisting of IT and complementary organizational changes, an implementable solution with minimal misfits with the existing organization, and an organization primed to appropriate the potential bene¢ts of the technochange solution. With hard work and care, the combined IT project management plus organizational change approach can be made to work. However, an iterative, incremental approach to implementing technochange can be a better strategy in many situations. The essential characteristic of the technochange prototyping approach is that each phase involves both new IT functionality and related organizational changes, such as redesigned business processes, new performance metrics, and training. Journal of Information Technology (2004) 19, 4–20. doi:10.1057/palgrave.jit.2000002

An efficient FPGA-Based architecture for convolutional neural networks <=SEP=> The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification.

A BigData approach for classification and prediction of student result using MapReduce <=SEP=> In recent years the amount of data stored in educational database is growing rapidly. The stored database contains hidden information which if used aids improvement of student's performance and behaviour. In this paper predictive modelling approach is used for extracting this hidden information. Data is collected, a predictive model is formulated, predictions are made, and the model is validated as additional data becomes available. The predictive models will help the instructor to understand how well or how poorly the students in his/her class will perform, and hence the instructor can choose proper pedagogical and instructional interventions to enhance student learning outcomes. The implementation is done in Hadoop framework with MapReduce and Revolutionary R Enterprise RRE.

“ Proof-of-Work ” Proves Not to Work version 0 . 1 <=SEP=> A frequently proposed method of reducing unsolicited bulk email (“spam”) is for senders to pay for each email they send. Proof-ofwork schemes avoid charging real money by requiring senders to demonstrate that they have expended processing time in solving a cryptographic puzzle. We attempt to determine how difficult that puzzle should be so as to be effective in preventing spam. We analyse this both from an economic perspective, “how can we stop it being cost-effective to send spam”, and from a security perspective, “spammers can access insecure end-user machines and will steal processing cycles to solve puzzles”. Both analyses lead to similar values of puzzle difficulty. Unfortunately, realworld data from a large ISP shows that these difficulty levels would mean that significant numbers of senders of legitimate email would be unable to continue their current levels of activity. We conclude that proof-of-work will not be a solution to the problem of spam.