Query: A model based path planning algorithm for self-driving cars in dynamic environment

Relevant passages:
EFFICIENT CONSTRAINED PATH PLANNING VIA SEARCH IN STATE LATTICES <=SEP=> We propose a novel approach to constrained path planning that is based on a special search space which efficiently encodes feasible paths. The paths are encoded implicitly as connections between states, but only feasible and local connections are included. Once this search space is developed, we systematically generate a near-minimal set of spatially distinct path primitives. This set expresses the local connectivity of constrained motions and also eliminates redundancies. The set of primitives is used to define heuristic search, and thereby create a very efficient path planner at the chosen resolution. We also discuss a wide variety of space and terrestrial robotics applications where this motion planner can be especially useful.

Path Planning using a Dynamic Vehicle Model <=SEP=> This paper addresses the problem of path planning using a dynamic vehicle model. Previous works which include a basic kinematic model generate paths that are only realistic at very low speed. By considering higher vehicle speed during navigation, the vehicle can significantly deviate from the planned trajectory. Consequently, the planned path becomes unusable for the mission achievement. So, to bridge a gap between planning and navigation, we propose a realistic path planner based on a dynamic vehicle model

Autonomous local path planning for a mobile robot using a genetic algorithm <=SEP=> This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

Real-time obstacle avoidance for manipulators and mobile robots <=SEP=> 

Reactive Nonholonomic Trajectory Generation via Parametric Optimal Control <=SEP=> There are many situations for which a feasible nonholonomic motion plan must be generated immediately based on real-time perceptual information. Parametric trajectory representations limit computation because they reduce the search space for solutions (at the cost of potentially introducing suboptimality). The use of any parametric trajectory model converts the optimal control formulation into an equivalent nonlinear programming problem. In this paper, curvature polynomials of arbitrary order are used as the assumed form of solution. Polynomials sacrifice little in terms of spanning the set of feasible controls while permitting an expression of the general solution to the system dynamics in terms of decoupled quadratures. These quadratures are then readily linearized to express the necessary conditions for optimality. Resulting trajectories are convenient to manipulate and execute in vehicle controllers and they can be computed with a straightforward numerical procedure in real time. KEY WORDS—mobile robots, car-like robots, trajectory generation, curve generation, nonholonomic, clothoid, cornu spiral, optimal control

MINDFULNESS AND TRAUMA: IMPLICATIONS FOR TREATMENT <=SEP=> Mindfulness, originally a construct used in Eastern spiritual and philosophical traditions, has found new utility in psychotherapy practice. Mindfulness practice has been recently applied to treatments of several psychological and health related problems, and research is showing successful outcomes in psychological interventions incorporating mindfulness practices. Several schools of psychotherapy have theorized why mindfulness may be an effective intervention. One population which would theoretically be benefited by mindfulness practice in treatment consists of those individuals who have experienced traumatic events and are exhibiting post-traumatic stress disorder and/or related correlates of past trauma. The present paper gives a general review of the application of mindfulness to clinical psychology interventions. Additionally, we explain how mindfulness is applicable to our integrative behavioral approach to treating trauma and its sequelae. Specifically, this paper will (a) give a general overview of the conceptions and applications of mindfulness to psychology and psychotherapy and provide a brief account of the concepts origins in eastern traditions; (b) discuss the theoretical conceptualization of clinical problems that may relate to the longterm correlates of trauma; (c) describe how mindfulness, acceptance and the therapeutic relationship can address trauma symptoms and discuss a modified treatment approach for trauma survivors that incorporates mindfulness and acceptance practices into traditional exposure treatment. Address correspondence to Victoria Follette, Department of Psychology/296, University of Nevada, Reno, NV, 89557, USA; e-mail: vmf@unr.edu. Journal of Rational-Emotive & Cognitive-Behavior Therapy, Vol. 24, No. 1, Spring 2006 ( 2006) DOI: 10.1007/s10942-006-0025-2 Published Online: August 2, 2006 45 2006 Springer Science+Business Media, Inc. Understanding the long-term effects of traumatic experiences and identifying appropriate treatment for trauma survivors continue to be complex and at times controversial tasks (Brewin, 2003). While cognitive-behavioral treatments are generally considered to have the most significant empirical support for the treatment of trauma (American Psychiatric Association Work Group on ASD and PTSD, 2004; Foa, Keane, & Friedman, 2000), practices associated with the treatment of the broader range of trauma related problems continue to evolve. Our understanding of trauma symptoms has expanded to include the knowledge that outcomes associated with traumatic experiences are not limited to Post-traumatic Stress Disorder (PTSD) but rather extend across a range of intraand interpersonal problems. Therefore, in our work with trauma survivors, we have conceptualized trauma symptomology in terms of functional classes of behavior, rather than focusing on a syndromal classification of symptoms (Follette & Naugle, 2006). While a great deal is known about the treatment of basic anxiety related phenomena associated with traumatic experiences, there are a number of lacunae in our knowledge about the treatment of the broader range of problems associated with a trauma history. Certainly the current repertoire of cognitive behavioral treatments for trauma survivors has demonstrated efficacy. However, we believe that the addition of mindfulness related treatment interventions will enhance our ability to treat people who have difficulties associated with having experienced a traumatic event. The theoretical work arising from our conceptualization of functional domains of behavior has informed our present integrative behavioral treatment approach. In our approach, we work to influence behavior change by increasing both acceptance strategies and skillful behavior. Mindfulness is a core component of both of these approaches. In the present paper, we will briefly address the historical roots of mindfulness, with its origins in eastern religious traditions. We will present a short review of the application of mindfulness to clinical psychology interventions. Finally, we will discuss our view of the utility of mindfulness in our integrative behavioral approach to treating trauma and its sequelae. MINDFULNESS IN EASTERN RELIGIOUS TRADITIONS In discussing the integration of Buddhist teachings with Western psychological practices, it should be noted that there are 46 Journal of Rational-Emotive & Cognitive-Behavior Therapy

Measuring User Engagement <=SEP=> User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus “in the wild”), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users’ motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of “good” measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world.

A 95% Efficient Normally-Off GaN-on-Si HEMT Hybrid-IC Boost-Converter with 425-W Output Power at 1 MHz <=SEP=> A 2:1 351 V hard-switched boost converter was constructed using high-voltage GaN high-electron-mobility transistors grown on Si substrates and GaN Schottky diodes grown on Sapphire substrates. The high speed and low on-resistance of the GaN devices enables extremely fast switching times and low losses, resulting in a high conversion efficiency of 95% with 425-W output power at 1 MHz. The boost converter has a power density of 175 W/in3. To our knowledge, these results are the best reported on GaN devices, and the highest for 1MHz switching.

Episodic Memory and Beyond: The Hippocampus and Neocortex in Transformation. <=SEP=> The last decade has seen dramatic technological and conceptual changes in research on episodic memory and the brain. New technologies, and increased use of more naturalistic observations, have enabled investigators to delve deeply into the structures that mediate episodic memory, particularly the hippocampus, and to track functional and structural interactions among brain regions that support it. Conceptually, episodic memory is increasingly being viewed as subject to lifelong transformations that are reflected in the neural substrates that mediate it. In keeping with this dynamic perspective, research on episodic memory (and the hippocampus) has infiltrated domains, from perception to language and from empathy to problem solving, that were once considered outside its boundaries. Using the component process model as a framework, and focusing on the hippocampus, its subfields, and specialization along its longitudinal axis, along with its interaction with other brain regions, we consider these new developments and their implications for the organization of episodic memory and its contribution to functions in other domains.

Solving sybil attacks using evolutionary game theory <=SEP=> Recommender systems have become quite popular recently. However, such systems are vulnerable to several types of attacks that target user ratings. One such attack is the Sybil attack where an entity masquerades as several identities with the intention of diverting user ratings. In this work, we propose evolutionary game theory as a possible solution to the Sybil attack in recommender systems. After modeling the attack, we use replicator dynamics to solve for evolutionary stable strategies. Our results show that under certain conditions that are easily achievable by a system administrator, the probability of an attack strategy drops to zero implying degraded fitness for Sybil nodes that eventually die out.

Application of spiking neural networks and the bees algorithm to control chart pattern recognition <=SEP=> Statistical process control (SPC) is a method for improving the quality o f products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types o f charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation o f artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application o f SNN techniques to control chart pattern recognition. It is designed to present an analysis o f the existing learning algorithms o f SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation o f neural networks. This thesis focuses on the architecture and the learning procedure o f the network. Four new learning algorithms are presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim o f the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.

Design of a smart parking system using wireless sensor network <=SEP=> In this article it is included the design of a smart parking system, the base was the current system "Sistema integrado de Estacionamiento (SIMERT)" and the mobility in Loja - Ecuador, with the use of a sensor network and wireless technology based in Zigbee 900 and Digimesh 2.4 GHz. To the feasibility of the project it was considered a technological platform that could be adapted to the city conditions and that be friendly with the environment, furthermore the suggested solution gives us connectivity with a Web application for integrated parking and usability of administrators and final users.

Automated Detection of Logical Errors in Programs <=SEP=> Research and industrial experience reveal that code reviews as a part of software inspection might be the most cost-effective technique a team can use to reduce defects. Tools that automate code inspection mostly focus on the detection of a priori known defect patterns and security vulnerabilities. Automated detection of logical errors, due to a faulty implementation of applications’ functionality is a relatively uncharted territory. Automation can be based on profiling the intended behavior behind the source code. In this paper, we present a new code profiling method that combines an information flow analysis, the crosschecking of dynamic invariants with symbolic execution, and the use of fuzzy logic. Our goal is to detect logical errors and exploitable vulnerabilities. The theoretical underpinnings and the practical implementation of our approach are discussed. We test the APP_LogGIC tool that implements the proposed analysis on two realworld applications. The results show that profiling the intended program behavior is feasible in diverse applications. We discuss the heuristics used to overcome the problem of state space explosion and of the large data sets. Code metrics and test results are provided to demonstrate the effectiveness of the approach.

An algorithm to determine peer-reviewers <=SEP=> The peer-review process is the most widely accepted certification mechanism for officially accepting the written results of researchers within the scientific community. An essential component of peer-review is the identification of competent referees to review a submitted manuscript. This article presents an algorithm to automatically determine the most appropriate reviewers for a manuscript by way of a co-authorship network data structure and a relative-rank particle-swarm algorithm. This approach is novel in that it is not limited to a pre-selected set of referees, is computationally efficient, requires no human-intervention, and, in some instances, can automatically identify conflict of interest situations. A useful application of this algorithm would be to open commentary peer-review systems because it provides a weighting for each referee with respects to their expertise in the domain of a manuscript. The algorithm is validated using referee bid data from the 2005 Joint Conference on Digital Libraries.

Health information systems: a survey of frameworks for developing countries. <=SEP=> OBJECTIVES
The objective of this paper is to perform a survey of excellent research on health information systems (HIS) analysis and design, and their underlying theoretical frameworks. It classifies these frameworks along major themes, and analyzes the different approaches to HIS development that are practical in resource-constrained environments.


METHOD
Literature review based on PubMed citations and conference proceedings, as well as Internet searches on information systems in general, and health information systems in particular.


RESULTS
The field of health information systems development has been studied extensively. Despite this, failed implementations are still common. Theoretical frameworks for HIS development are available that can guide implementers.


CONCLUSION
As awareness, acceptance, and demand for health information systems increase globally, the variety of approaches and strategies will also follow. For developing countries with scarce resources, a trial-and-error approach can be very costly. Lessons from the successes and failures of initial HIS implementations have been abstracted into theoretical frameworks. These frameworks organize complex HIS concepts into methodologies that standardize techniques in implementation. As globalization continues to impact healthcare in the developing world, demand for more responsive health systems will become urgent. More comprehensive frameworks and practical tools to guide HIS implementers will be imperative.

Applying Deep Learning to the Newsvendor Problem <=SEP=> The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. If the probability distribution of the demand is known, the problem can be solved analytically. However, approximating the probability distribution is not easy and is prone to error; therefore, the resulting solution to the newsvendor problem may be not optimal. To address this issue, we propose an algorithm based on deep learning that optimizes the order quantities for all products based on features of the demand data. Our algorithm integrates the forecasting and inventory-optimization steps, rather than solving them separately, as is typically done, and does not require knowledge of the probability distributions of the demand. Numerical experiments on real-world data suggest that our algorithm outperforms other approaches, including data-driven and machine learning approaches, especially for demands with high volatility. Finally, in order to show how this approach can be used for other inventory optimization problems, we provide an extension for (r,Q) policies.

Sensor fusion for robot control through deep reinforcement learning <=SEP=> Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.

Low-resource routing attacks against tor <=SEP=> Tor has become one of the most popular overlay networks for anonymizing TCP traffic. Its popularity is due in part to its perceived strong anonymity properties and its relatively low latency service. Low latency is achieved through Torâ s ability to balance the traffic load by optimizing Tor router selection to probabilistically favor routers with highbandwidth capabilities.
 We investigate how Torâ s routing optimizations impact its ability to provide strong anonymity. Through experiments conducted on PlanetLab, we show the extent to which routing performance optimizations have left the system vulnerable to end-to-end traffic analysis attacks from non-global adversaries with minimal resources. Further, we demonstrate that entry guards, added to mitigate path disruption attacks, are themselves vulnerable to attack. Finally, we explore solutions to improve Torâ s current routing algorithms and propose alternative routing strategies that prevent some of the routing attacks used in our experiments.

Arches: a Framework for Modeling Complex Terrains <=SEP=> In this paper, we present a framework for representing complex terrains with such features as overhangs, arches and caves and including different materials such as sand and rocks. Our hybrid model combines a volumetric discrete data structure that stores the different materials and an implicit representation for sculpting and reconstructing the surface of the terrain. Complex scenes can be edited and sculpted interactively with high level tools. We also propose an original rock generation technique that enables us to automatically generate complex rocky sceneries with piles of rocks without any computationally demanding physically-based simulation.

BERDS-BERkeley EneRgy Disaggregation Data Set <=SEP=> We first review some of the suggested methods for energy disaggregation. We then provide a real-life data set for researchers to help developing novel energy disaggregation algorithms. We also present a publicly available experimental data set captured from a typical building on the University of California, Berkeley campus.

Action Search: Learning to Search for Human Activities in Untrimmed Videos <=SEP=> Traditional approaches for action detection use trimmed data to learn sophisticated action detector models. Although these methods have achieved great success at detecting human actions, we argue that huge information is discarded when ignoring the process, through which this trimmed data is obtained. In this paper, we propose Action Search, a novel approach that mimics the way people annotate activities in video sequences. Using a Recurrent Neural Network, Action Search can efficiently explore a video and determine the time boundaries during which an action occurs. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently but also accurately find human activities, outperforming stateof-the-art methods.

Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation <=SEP=> Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type and/or a limited number of queries. On the contrary, k-anonymity-like data releases make no assumptions on the uses of the protected data and, thus, do not restrict the number and type of doable analyses. Recently, some authors have proposed mechanisms to offer general-purpose differentially private data releases. This paper extends such works with a specific focus on the preservation of the utility of the protected data. Our proposal builds on microaggregation-based anonymization, which is more flexible and utility-preserving than alternative anonymization methods used in the literature, in order to reduce the amount of noise needed to satisfy differential privacy. In this way, we improve the utility of differentially private data releases. Moreover, the noise reduction we achieve does not depend on the size of the data set, but just on the number of attributes to be protected, which is a more desirable behavior for large data sets. The utility benefits brought by our proposal are empirically evaluated and compared with related works for several data sets and metrics.

The neuropsychiatry of the cerebellum — insights from the clinic <=SEP=> A central aspect of the cerebellar cognitive affective syndrome is the dysregulation of affect that occurs when lesions involve the ‘limbic cerebellum’ (vermis and fastigial nucleus). In this case series we describe neuropsychiatric disturbances in adults and children with congenital lesions including cerebellar agenesis, dysplasia, and hypoplasia, and acquired conditions including cerebellar stroke, tumor, cerebellitis, trauma, and neurodegenerative disorders. The behaviors that we witnessed and that were described by patients and families included distractibility and hyperactivity, impulsiveness, disinhibition, anxiety, ritualistic and stereotypical behaviors, illogical thought and lack of empathy, as well as aggression and irritability. Ruminative and obsessive behaviors, dysphoria and depression, tactile defensiveness and sensory overload, apathy, childlike behavior, and inability to appreciate social boundaries and assign ulterior motives were also evident. We grouped these disparate neurobehavioral profiles into five major domains, characterized broadly as disorders of attentional control, emotional control, and social skill set as well as autism spectrum disorders, and psychosis spectrum disorders. Drawing on our dysmetria of thought hypothesis, we conceptualized the symptom complexes within each putative domain as reflecting either exaggeration (overshoot, hypermetria) or diminution (hypotonia, or hypometria) of responses to the internal or external environment. Some patients fluctuated between these two states. We consider the implications of these neurobehavioral observations for the care of patients with ataxia, discuss the broader role of the cerebellum in the pathogenesis of these neuropsychiatric symptoms, and revisit the possibility of using cerebellar stimulation to treat psychiatric disorders by enhancing cerebellar modulation of cognition and emotion.

Neuronal Specificity of Acupuncture in Alzheimer's Disease and Mild Cognitive Impairment Patients: A Functional MRI Study <=SEP=> Although acupuncture is considered to be effective and safe for Alzheimer's disease (AD) and mild cognitive impairment (MCI), the mechanism underlying its therapeutic effect is still unknown. Most studies clarifying the neuronal pathway produced by acupuncture were still applied to healthy subjects with limited single acupuncture point stimulation, which was inconsistency with clinical practice. Thus, in our present study, we investigate the differences between brain activity changes in AD and MCI patients caused by multi-acupuncture point Siguan (four gates), in order to provide visualized evidence for neuronal specificity of clinical acupuncture. Forty-nine subjects were recruited, including 21 AD patients, 14 MCI patients, and 14 healthy controls (HC). AD and MCI patients were randomly divided into two groups, respectively: real acupuncture point group (14 AD and 8 MCI) and sham acupuncture point group (7 AD and 6 MCI). We adopted a 16-minute, single-block, experimental design for acquiring functional MRI images. We found, in AD and MCI patients, Siguan (four gates) elicited extensive activations and deactivations in cognitive-related areas, visual-related areas, the sensorimotor-related area, basal ganglia, and cerebellum. Compared with HC, AD and MCI patients showed similar activations in cognitive-related brain areas (inferior frontal gyrus, supramarginal gyrus, and rolandic operculum) as well as deactivations in cognitive-related areas, visual-related areas, basal ganglia, and cerebellum, which were not found in HC. Compared with sham acupuncture points, real acupuncture points produced more specific brain changes with both activated and deactivated brain activities in AD and MCI. The preliminary results in our study verified the objective evidence for neuronal specificity of acupuncture in AD and MCI patients.

Roombots—Towards decentralized reconfiguration with self-reconfiguring modular robotic metamodules <=SEP=> This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots (RB) metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules—two Roombots modules connected serially—that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planner.

Social Persuasion in Online and Physical Networks <=SEP=> Social persuasion to influence the actions, beliefs, and behaviors of individuals, embedded in a social network, has been widely studied. It has been applied to marketing, healthcare, sustainability, political campaigns, and public policy. Traditionally, there has been a separation between physical (offline) and cyber (online) worlds. While persuasion methods in the physical world focused on strong interpersonal trust and design principles, persuasion methods in the online world were rich on data-driven analysis and algorithms. Recent trends including Internet of Things, “big data,” and smartphone adoption point to the blurring divide between the cyber world and the physical world in the following ways. Fine grained data about each individual's location, situation, social ties, and actions are collected and merged from different sources. The messages for persuasion can be transmitted through both worlds at suitable times and places. The impact of persuasion on each individual is measurable. Hence, we posit that the social persuasion will soon be able to span seamlessly across these worlds and will be able to employ computationally and empirically rigorous methods to understand and intervene in both cyber and physical worlds. Several early examples indicate that this will impact the fundamental facets of persuasion including who, how, where, and when, and pave way for multiple opportunities as well as research challenges.

Improving Interactional Organizational Research : A Model of Person-Organization Fit <=SEP=> In order for researchers to understand and predict behavior, they must consider both person and situation factors and how these factors interact. Even though organization researchers have developed interactional models, many have overemphasized either person or situation components, and most have failed to consider the effects that persons have on situations. This paper presents criteria for improving interactional models and a model of person-organization fit, which satisfies these criteria. Using a Q-sort methodology, individual value profiles are compared to organizational value profiles to determine fit and to predict changes in values, norms, and behaviors.

Constraint Programming in OPL <=SEP=> OPL is a modeling language for mathematical programming and combinatorial optimization problems. It is the rst modeling language to combine high-level algebraic and set notations from model-ing languages with a rich constraint language and the ability to specify search procedures and strategies that is the essence of constraint programming. In addition, OPL models can be controlled and composed using OPLSCRIPT, a script language that simpliies the development of applications that solve sequences of models, several instances of the same model, or a combination of both as in column-generation applications. This paper illustrates some of the functionalities of OPL for constraint programming using frequency allocation, sport-scheduling, and job-shop scheduling applications. It also illustrates how OPL models can be composed using OPLSCRIPT on a simple connguration example.

Recycling ambient microwave energy with broad-band rectenna arrays <=SEP=> This paper presents a study of reception and rectification of broad-band statistically time-varying low-power-density microwave radiation. The applications are in wireless powering of industrial sensors and recycling of ambient RF energy. A 64-element dual-circularly-polarized spiral rectenna array is designed and characterized over a frequency range of 2-18 GHz with single-tone and multitone incident waves. The integrated design of the antenna and rectifier, using a combination of full-wave electromagnetic field analysis and harmonic balance nonlinear circuit analysis, eliminates matching and filtering circuits, allowing for a compact element design. The rectified dc power and efficiency is characterized as a function of dc load and dc circuit topology, RF frequency, polarization, and incidence angle for power densities between 10/sup -5/-10/sup -1/ mW/cm/sup 2/. In addition, the increase in rectenna efficiency for multitone input waves is presented.

Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review <=SEP=> Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game’s features, behaviors, and scenarios in realtime, depending on the player’s skill, so that the player, when the game is very simple, does not feel bored or frustrated, when it is very difficult. The intent of the DDA is to keep the player engrossed till the end and to provide him/her with a challenging experience. In traditional games, difficulty levels increase linearly or stepwise during the course of the game. The features such as frequency, starting levels, or rates can be set only at the beginning of the game by choosing a level of difficulty. This can, however, result in a negative experience for players as they try to map a predecided learning curve. DDA attempts to solve this problem by presenting a customized solution for the gamers. This paper provides a review of the current approaches to DDA.