Query: Control Flow Analysis for Reverse Engineering of Sequence Diagrams

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Optimizing Java Bytecode Using the Soot Framework: Is It Feasible? <=SEP=> This paper presents Soot, a framework for optimizing Java bytecode. The framework is implemented in Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of Java's stack-based bytecode; Jimple, a typed three-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple. Our approach to class le optimization is to rst convert the stack-based bytecode into Jimple, a three-address form more amenable to traditional program optimization, and then convert the optimized Jimple back to bytecode. In order to demonstrate that our approach is feasible, we present experimental results showing the e ects of processing class les through our framework. In particular, we study the techniques necessary to effectively translate Jimple back to bytecode, without losing performance. Finally, we demonstrate that class le optimization can be quite e ective by showing the results of some basic optimizations using our framework. Our experiments were done on ten benchmarks, including seven SPECjvm98 benchmarks, and were executed on ve di erent Java virtual machine implementations.

The Java Language Specification <=SEP=> class RawMembers<T> extends NonGeneric implements Collection<String> { static Collection<NonGeneric> cng = new ArrayList<NonGeneric>(); public static void main(String[] args) { RawMembers rw = null; Collection<Number> cn = rw.myNumbers(); // OK Iterator<String> is = rw.iterator(); // Unchecked warning Collection<NonGeneric> cnn = rw.cng; // OK, static member } } In this program (which is not meant to be run), RawMembers<T> inherits the method: Iterator<String> iterator() from the Collection<String> superinterface. The raw type RawMembers inherits iterator() from Collection, the erasure of Collection<String>, which means that the return type of iterator() in RawMembers is Iterator. As a result, the attempt to 4.9 Intersection Types TYPES, VALUES, AND VARIABLES 70 assign rw.iterator() to Iterator<String> requires an unchecked conversion, so a compile-time unchecked warning is issued. In contrast, RawMembers inherits myNumbers() from the NonGeneric class whose erasure is also NonGeneric. Thus, the return type of myNumbers() in RawMembers is not erased, and the attempt to assign rw.myNumbers() to Collection<Number> requires no unchecked conversion, so no compile-time unchecked warning is issued. Similarly, the static member cng retains its parameterized type even when accessed through a object of raw type. Note that access to a static member through an instance is considered bad style and is discouraged. This example reveals that certain members of a raw type are not erased, namely static members whose types are parameterized, and members inherited from a non-generic supertype. Raw types are closely related to wildcards. Both are based on existential types. Raw types can be thought of as wildcards whose type rules are deliberately unsound, to accommodate interaction with legacy code. Historically, raw types preceded wildcards; they were first introduced in GJ, and described in the paper Making the future safe for the past: Adding Genericity to the Java Programming Language by Gilad Bracha, Martin Odersky, David Stoutamire, and Philip Wadler, in Proceedings of the ACM Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA 98), October 1998. 4.9 Intersection Types An intersection type takes the form T1 & ... & Tn (n > 0), where Ti (1 ≤ i ≤ n) are types. Intersection types can be derived from type parameter bounds (§4.4) and cast expressions (§15.16); they also arise in the processes of capture conversion (§5.1.10) and least upper bound computation (§4.10.4). The values of an intersection type are those objects that are values of all of the types Ti for 1 ≤ i ≤ n. Every intersection type T1 & ... & Tn induces a notional class or interface for the purpose of identifying the members of the intersection type, as follows: • For each Ti (1 ≤ i ≤ n), let Ci be the most specific class or array type such that Ti <: Ci. Then there must be some Ck such that Ck <: Ci for any i (1 ≤ i ≤ n), or a compile-time error occurs. • For 1 ≤ j ≤ n, if Tj is a type variable, then let Tj' be an interface whose members are the same as the public members of Tj; otherwise, if Tj is an interface, then let Tj' be Tj. TYPES, VALUES, AND VARIABLES Subtyping 4.10 71 • If Ck is Object, a notional interface is induced; otherwise, a notional class is induced with direct superclass Ck. This class or interface has direct superinterfaces T1', ..., Tn' and is declared in the package in which the intersection type appears. The members of an intersection type are the members of the class or interface it induces. It is worth dwelling upon the distinction between intersection types and the bounds of type variables. Every type variable bound induces an intersection type. This intersection type is often trivial, consisting of a single type. The form of a bound is restricted (only the first element may be a class or type variable, and only one type variable may appear in the bound) to preclude certain awkward situations coming into existence. However, capture conversion can lead to the creation of type variables whose bounds are more general, such as array types).

Reverse Engineering of the Interaction Diagrams from C++ Code <=SEP=> In object oriented programming, the functionalities of a system result from the interactions (message exchanges) among the objects allocated by the system. While designing object interactions is far more complex than designing the object structure in forward engineering, the problem of understanding object interactions during code evolution is even harder, because the related information is spread across the code. In this paper, a technique for the automatic extraction of UML interaction diagrams from C++ code is proposed. The algorithm is based on a static, conservative flow analysis, that approximates the behavior of the system in any execution and for any possible input. Applicability of the approach to large software is achieved by means of two mechanisms: partial analysis and focusing. Usage of our method on a real world, large C++ system confirmed its viability.

Compilers: Principles, Techniques, and Tools <=SEP=> 

A Fast Algorithm for Finding Dominators in a Flowgraph <=SEP=> A fast algorithm for finding dominators in a flowgraph is presented. The algorithm uses depth-first search and an efficient method of computing functions defined on paths in trees. A simple implementation of the algorithm runs in <italic>O</italic>(<italic>m</italic> log <italic>n</italic>) time, where <italic>m</italic> is the number of edges and <italic>n</italic> is the number of vertices in the problem graph. A more sophisticated implementation runs in <italic>O</italic>(<italic>m</italic>α(<italic>m</italic>, <italic>n</italic>)) time, where α(<italic>m</italic>, <italic>n</italic>) is a functional inverse of Ackermann's function.
Both versions of the algorithm were implemented in Algol W, a Stanford University version of Algol, and tested on an IBM 370/168. The programs were compared with an implementation by Purdom and Moore of a straightforward <italic>O</italic>(<italic>mn</italic>)-time algorithm, and with a bit vector algorithm described by Aho and Ullman. The fast algorithm beat the straightforward algorithm and the bit vector algorithm on all but the smallest graphs tested.

P300 amplitude is determined by target-to-target interval. <=SEP=> P300 event-related brain potential (ERP) measures are affected by target stimulus probability, the number of nontargets preceding the target in the stimulus sequence structure, and interstimulus interval (ISI). Each of these factors contributes to the target-to-target interval (TTI), which also has been found to affect P300. The present study employed a variant of the oddball paradigm and manipulated the number of preceding nontarget stimuli (0, 1, 2, 3) and ISI (1, 2, 4 s) in order to systematically assess TTI effects on P300 values from auditory and visual stimuli. Number of preceding nontargets generally produced stronger effects than ISI in a manner suggesting that TTI determined P300 measures: Amplitude increased as TTI increased for both auditory and visual stimulus conditions, whereas latency tended to decrease with increased TTI. The finding that TTI is a critical determinant of P300 responsivity is discussed within a resource allocation theoretical framework.

Ultra Low Power Wake-Up Radios: A Hardware and Networking Survey <=SEP=> In wireless environments, transmission and reception costs dominate system power consumption, motivating research effort on new technologies capable of reducing the footprint of the radio, paving the way for the Internet of Things. The most important challenge is to reduce power consumption when receivers are idle, the so called idle-listening cost. One approach proposes switching off the main receiver, then introduces new wake-up circuitry capable of detecting an incoming transmission, optionally discriminating the packet destination using addressing, then switching on the main radio only when required. This wake-up receiver technology represents the ultimate frontier in low power radio communication. In this paper, we present a comprehensive literature review of the research progress in wake-up radio (WuR) hardware and relevant networking software. First, we present an overview of the WuR system architecture, including challenges to hardware design and a comparison of solutions presented throughout the last decade. Next, we present various medium access control and routing protocols as well as diverse ways to exploit WuRs, both as an extension of pre-existing systems and as a new concept to manage low-power networking.

Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0 <=SEP=> Digital twins represent real objects or subjects with their data, functions, and communication capabilities in the digital world. As nodes within the internet of things, they enable networking and thus the automation of complex value-added chains. The application of simulation techniques brings digital twins to life and makes them experimentable; digital twins become experimentable digital twins (EDTs). Initially, these EDTs communicate with each other purely in the virtual world. The resulting networks of interacting EDTs model different application scenarios and are simulated in virtual testbeds, providing new foundations for comprehensive simulation-based systems engineering. Its focus is on EDTs, which become more detailed with every single application. Thus, complete digital representations of the respective real assets and their behaviors are created successively. The networking of EDTs with real assets leads to hybrid application scenarios in which EDTs are used in combination with real hardware, thus realizing complex control algorithms, innovative user interfaces, or mental models for intelligent systems.

Design, kinematics and dynamics modeling of a lower-limb walking assistant robot <=SEP=> This paper focuses on a lower limb walking assistant robot. This robot has been designed and modeled for two tasks of facilitating healthy people, and assisting patients for walking. First, basic concepts of the motion mechanism are presented. Next, its kinematics and dynamics equations have been developed. Kinematics analysis has been done using Denavit-Hartenberg scheme to obtain position, velocity and acceleration of center of mass (COM) of all links. Dynamics equations have been developed using recursive Lagrange method and finally inverse dynamics problem has been solved. Obtained simulation results will be discussed which validate the proposed model.

Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric <=SEP=> We describe and identify shortcomings in two statistics recently introduced to measure accuracy in text entry evaluations: the minimum string distance (MSD) error rate and keystrokes per character (KSPC). To overcome the weaknesses, a new framework for error analysis is developed and demonstrated. It combines the analysis of the presented text, input stream (keystrokes), and transcribed text. New statistics include a unified total error rate, combining two constituent error rates: the corrected error rate (errors committed but corrected) and the not corrected error rate (errors left in the transcribed text). The framework includes other measures including error correction efficiency, participant conscientiousness, utilised bandwidth, and wasted bandwidth. A text entry study demonstrating the new methodology is described.

Lyapunov Approach for the stabilization of the Inverted Spherical Pendulum <=SEP=> A nonlinear controller is presented for the stabilization of the spherical inverted pendulum system. The control strategy is based on the Lyapunov approach in conjunction with LaSalle's invariance principle. The proposed controller is able to bring the pendulum to the unstable upright equilibrium point with the position of the movable base at the origin. The obtained closed-loop system has a very large domain of attraction, that can be as large as desired, for any initial position of the pendulum which lies above the horizontal plane

A Data Structure for Dynamic Trees <=SEP=> We propose a data structure to maintain a collection of vertex-disjoint trees under a sequence of two kinds of operations: a link operation that combines two trees into one by adding an edge, and a cut operation that divides one tree into two by deleting an edge. Our data structure requires O(log n) time per operation when the time is amortized over a sequence of operations. Using our data structure, we obtain new fast algorithms for the following problems:
 (1) Computing deepest common ancestors.
 (2) Solving various network flow problems including finding maximum flows, blocking flows, and acyclic flows.
 (3) Computing certain kinds of constrained minimum spanning trees.
 (4) Implementing the network simplex algorithm for the transshipment problem.
 Our most significant application is (2); we obtain an O(mn log n)-time algorithm to find a maximum flow in a network of n vertices and m edges, beating by a factor of log n the fastest algorithm previously known for sparse graphs.

A survey of variability modeling in industrial practice <=SEP=> Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. However, little is known about the actual use of these techniques. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling. This lack of empirical data threatens the validity of existing techniques, and hinders their improvement. As part of our effort to improve empirical understanding of variability modeling, we present the results of a survey questionnaire distributed to industrial practitioners. These results provide insights into application scenarios and perceived benefits of variability modeling, the notations and tools used, the scale of industrial models, and experienced challenges and mitigation strategies.

NONCONVEX MODEL FOR FACTORING NONNEGATIVE MATRICES <=SEP=> We study the Nonnegative Matrix Factorization problem which approximates a nonnegative matrix by a low-rank factorization. This problem is particularly important in Machine Learning, and finds itself in a large number of applications. Unfortunately, the original formulation is ill-posed and NPhard. In this paper, we propose a row sparse model based on Row Entropy Minimization to solve the NMF problem under separable assumption which states that each data point is a convex combination of a few distinct data columns. We utilize the concentration of the entropy function and the `∞ norm to concentrate the energy on the least number of latent variables. We prove that under the separability assumption, our proposed model robustly recovers data columns that generate the dataset, even when the data is corrupted by noise. We empirically justify the robustness of the proposed model and show that it is significantly more robust than the state-ofthe-art separable NMF algorithms.

Multi-label classification by exploiting label correlations <=SEP=> a Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China b Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada c Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e School of Software, Jiangxi Agricultural University, Nanchang 330013, PR China

Variational Bayesian Inference for Big Data Marketing Models 1 <=SEP=> Hierarchical Bayesian approaches play a central role in empirical marketing as they yield individual-level parameter estimates that can be used for targeting decisions. MCMC methods have been the methods of choice for estimating hierarchical Bayesian models as they are capable of providing accurate individual-level estimates. However, MCMC methods are computationally prohibitive and do not scale well when applied to massive data sets that have become common in the current era of “Big Data”. We introduce to the marketing literature a new class of Bayesian estimation techniques known as variational Bayesian (VB) inference. These methods tackle the scalability challenge via a deterministic optimization approach to approximate the posterior distribution and yield accurate estimates at a fraction of the computational cost associated with simulation-based MCMC methods. We exploit and extend recent developments in variational Bayesian inference and highlight how two VB estimation approaches – Mean-field VB (that is analogous to Gibbs sampling) for conjugate models and Fixed-form VB (which is analogous to Metropolis-Hasting) for nonconjugate models – can be effectively combined for estimating complex marketing models. We also show how recent advances in parallel computing and in stochastic optimization can be used to further enhance the speed of these VB methods. Using simulated as well as real data sets, we apply the VB approaches to several commonly used marketing models (e.g. mixed linear, logit, selection, and hierarchical ordinal logit models), and demonstrate how the VB inference is widely applicable for marketing problems.

Uplift Modeling with Multiple Treatments and General Response Types <=SEP=> Randomized experiments have been used to assist decisionmaking in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods. Accepted: 2017 SIAM International Conference on Data

Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling <=SEP=> A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

Predicting Source Code Quality with Static Analysis and Machine Learning <=SEP=> This paper is investigating if it is possible to predict source code quality based on static analysis and machine learning. The proposed approach includes a plugin in Eclipse, uses a combination of peer review/human rating, static code analysis, and classification methods. As training data, public data and student hand-ins in programming are used. Based on this training data, new and uninspected source code can be accurately classified as “well written” or “badly written”. This is a step towards feedback in an interactive environment without peer assessment.

Traffic Accident Data Mining Using Machine Learning Paradigms <=SEP=> Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop a prediction model that automatically classifies the type of injury severity of various traffic accidents. These behavioral and roadway patterns are useful in the development of traffic safety control policy. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper presents some models to predict the severity of injury that occurred during traffic accidents using three machine-learning approaches. We considered neural networks trained using hybrid learning approaches, decision trees and a concurrent hybrid model involving decision trees and neural networks. Experiment results reveal that among the machine learning paradigms considered the hybrid decision tree-neural network approach outperformed the individual approaches.

Design Procedure of Dual-Stator Spoke-Array Vernier Permanent-Magnet Machines <=SEP=> The dual-stator spoke-array vernier permanent-magnet (DSSA VPM) machines proposed in the previous papers have been proven to be with high torque density and high power factor. However, the design procedure on the DSSA VPM machines has not been well established, and there is little design experience to be followed, which makes the DSSA VPM machine design quite difficult. This paper presents the detailed DSSA VPM machine design procedure including decision of design parameter initial values, analytical sizing equation, geometric size relationship, and so on. In order to get reasonable design parameter initial values which can reduce the number of design iteration loop, the influence of the key parameters, such as rotor/stator pole combination, slot opening, magnet thickness, etc., on the performances is analyzed based on the finite-element algorithm (FEA) in this paper, and the analysis results can be regarded as design experience during the selection process of the initial values. After that, the analytical sizing equation and geometric relationship formulas are derived and can be used to obtain and optimize the size data of the DSSA VPM machines with little time consumption. The combination of the analytical and FEA methods makes the design procedure time-effective and reliable. Finally, the design procedure is validated by experiments on a DSSA VPM prototype with 2000 N·m.

Agent-Based Modeling of the Spread of Influenza-Like Illness in an Emergency Department: A Simulation Study <=SEP=> The objective of this paper was to develop an agent based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C + + using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that be came infected in one day within the simulation was the dependent variable. The results suggest that within the given instance con text, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

The Sorcerer's Apprentice Guide to Fault Attacks <=SEP=> The effect of faults on electronic systems has been studied since the 1970s when it was noticed that radioactive particles caused errors in chips. This led to further research on the effect of charged particles on silicon, motivated by the aerospace industry, which was becoming concerned about the effect of faults in airborne electronic systems. Since then various mechanisms for fault creation and propagation have been discovered and researched. This paper covers the various methods that can be used to induce faults in semiconductors and exploit such errors maliciously. Several examples of attacks stemming from the exploiting of faults are explained. Finally a series of countermeasures to thwart these attacks are described.

Health and well-being benefits of spending time in forests: systematic review <=SEP=> BACKGROUND
Numerous studies have reported that spending time in nature is associated with the improvement of various health outcomes and well-being. This review evaluated the physical and psychological benefits of a specific type of exposure to nature, forest therapy.


METHOD
A literature search was carried out using MEDLINE, PubMed, ScienceDirect, EMBASE, and ProQuest databases and manual searches from inception up to December 2016. Key words: "Forest" or "Shinrin -Yoku" or "Forest bath" AND "Health" or "Wellbeing". The methodological quality of each randomized controlled trials (RCTs) was assessed according to the Cochrane risk of bias (ROB) tool.


RESULTS
Six RCTs met the inclusion criteria. Participants' ages ranged from 20 to 79 years. Sample size ranged from 18 to 99. Populations studied varied from young healthy university students to elderly people with chronic disease. Studies reported the positive impact of forest therapy on hypertension (n = 2), cardiac and pulmonary function (n = 1), immune function (n = 2), inflammation (n = 3), oxidative stress (n = 1), stress (n = 1), stress hormone (n = 1), anxiety (n = 1), depression (n = 2), and emotional response (n = 3). The quality of all studies included in this review had a high ROB.


CONCLUSION
Forest therapy may play an important role in health promotion and disease prevention. However, the lack of high-quality studies limits the strength of results, rendering the evidence insufficient to establish clinical practice guidelines for its use. More robust RCTs are warranted.

Character-based feature extraction with LSTM networks for POS-tagging task <=SEP=> In this paper we describe a work in progress on designing the continuous vector space word representations able to map unseen data adequately. We propose a LSTM-based feature extraction layer that reads in a sequence of characters corresponding to a word and outputs a single fixed-length real-valued vector. We then test our model on a POS tagging task on four typologically different languages. The results of the experiments suggest that the model can offer a solution to the out-of-vocabulary words problem, as in a comparable setting its OOV accuracy improves over that of a state of the art tagger.

Source Localization in Wireless Sensor Networks From Signal Time-of-Arrival Measurements <=SEP=> Recent advances in wireless sensor networks have led to renewed interests in the problem of source localization. Source localization has broad range of applications such as emergency rescue, asset inventory, and resource management. Among various measurement models, one important and practical source signal measurement is the received signal time of arrival (TOA) at a group of collaborative wireless sensors. Without time-stamp at the transmitter, in traditional approaches, these received TOA measurements are subtracted pairwise to form time-difference of arrival (TDOA) data for source localization, thereby leading to a 3-dB loss in signal-to-noise ratio (SNR). We take a different approach by directly applying the original measurement model without the subtraction preprocessing. We present two new methods that utilize semidefinite programming (SDP) relaxation for direct source localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of source localization based on the direct TOA data over time-difference preprocessing.

Design, modeling and control of an omni-directional aerial vehicle <=SEP=> In this paper we present the design and control of a novel six degrees-of-freedom aerial vehicle. Based on a static force and torque analysis for generic actuator configurations, we derive an eight-rotor configuration that maximizes the vehicle's agility in any direction. The proposed vehicle design possesses full force and torque authority in all three dimensions. A control strategy that allows for exploiting the vehicle's decoupled translational and rotational dynamics is introduced. A prototype of the proposed vehicle design is built using reversible motor-propeller actuators and capable of flying at any orientation. Preliminary experimental results demonstrate the feasibility of the novel design and the capabilities of the vehicle.

High Level Sensor Data Fusion Approaches For Object Recognition In Road Environment <=SEP=> Application of high level fusion approaches demonstrate a sequence of significant advantages in multi sensor data fusion and automotive safety fusion systems are no exception to this. High level fusion can be applied to automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper two specific high level data fusion approaches are described including a brief architectural and algorithmic presentation. These approaches differ mainly in their data association part: (a) track level fusion approach solves it with the point to point association with emphasis on object continuity and multidimensional assignment, and (b) grid based fusion approach that proposes a generic way to model the environment and to perform sensor data fusion. The test case for these approaches is a multi sensor equipped PReVENT/ProFusion2 truck demonstrator vehicle.

A simple stimulus generator for testing single-channel monopulse processor <=SEP=> The monopulse antenna tracking system is the preferred choice where higher pointing accuracy is required because of its inherent advantages. There are three possible configurations for realizing monopulse tracking system. One of the configurations is called single channel monopulse tracking system as it requires only one down converter chain. In this configuration, the sum and both (AZ/EL) error signals are combined to reduce the required number of down converters. A single channel monopulse processor is vital subsystem of a single channel monopulse tracking system which extracts the pointing error information from the IF signal. During the development, these processors need to be tested for its functionality in the laboratory which requires a stimulus generator. The stimulus generator generates an IF signal which mimics the real time signal and can be used for debugging and functional verification. This paper presents a simple approach for realizing a stimulus generator for single channel monopulse processor. A stimulus generator has been developed using this approach and has been used for laboratory testing of a single channel monopulse processor. The tested single channel monopulse processor has been successfully integrated with the earth station antenna tracking chain at NRSC, Hyderabad, India and used for tracking the LEO satellites.

Pulsed radiofrequency treatment in interventional pain management: mechanisms and potential indications—a review <=SEP=> The objective of this review is to evaluate the efficacy of Pulsed Radiofrequency (PRF) treatment in chronic pain management in randomized clinical trials (RCTs) and well-designed observational studies. The physics, mechanisms of action, and biological effects are discussed to provide the scientific basis for this promising modality. We systematically searched for clinical studies on PRF. We searched the MEDLINE (PubMed) and EMBASE database, using the free text terms: pulsed radiofrequency, radio frequency, radiation, isothermal radiofrequency, and combination of these. We classified the information in two tables, one focusing only on RCTs, and another, containing prospective studies. Date of last electronic search was 30 May 2010. The methodological quality of the presented reports was scored using the original criteria proposed by Jadad et al. We found six RCTs that evaluated the efficacy of PRF, one against corticosteroid injection, one against sham intervention, and the rest against conventional RF thermocoagulation. Two trials were conducted in patients with lower back pain due to lumbar zygapophyseal joint pain, one in cervical radicular pain, one in lumbosacral radicular pain, one in trigeminal neuralgia, and another in chronic shoulder pain. From the available evidence, the use of PRF to the dorsal root ganglion in cervical radicular pain is compelling. With regards to its lumbosacral counterpart, the use of PRF cannot be similarly advocated in view of the methodological quality of the included study. PRF application to the supracapular nerve was found to be as efficacious as intra-articular corticosteroid in patients with chronic shoulder pain. The use of PRF in lumbar facet arthropathy and trigeminal neuralgia was found to be less effective than conventional RF thermocoagulation techniques.