Query: Reward-estimation variance elimination in sequential decision processes

Relevant passages:
High-Dimensional Continuous Control Using Generalized Advantage Estimation <=SEP=> This paper is concerned with developing policy gradient methods that gracefully scale up to challenging problems with high-dimensional state and action spaces. Towards this end, we develop a scheme that uses value functions to substantially reduce the variance of policy gradient estimates, while introducing a tolerable amount of bias. This scheme, which we call generalized advantage estimation (GAE), involves using a discounted sum of temporal difference residuals as an estimate of the advantage function, and can be interpreted as a type of automated cost shaping. It is simple to implement and can be used with a variety of policy gradient methods and value function approximators. Along with this variance-reduction scheme, we use trust region algorithms to optimize the policy and value function, both represented as neural networks. We present experimental results on a number of highly challenging 3D locomotion tasks, where our approach learns complex gaits for bipedal and quadrupedal simulated robots. We also learn controllers for the biped getting up off the ground. In contrast to prior work that uses hand-crafted low-dimensional policy representations, our neural network policies map directly from raw kinematics to joint torques.

Policy Gradient Methods for Robotics <=SEP=> The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm

Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates <=SEP=> Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.

PILCO: A Model-Based and Data-Efficient Approach to Policy Search <=SEP=> In this paper, we introduce pilco, a practical, data-efficient model-based policy search method. Pilco reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, pilco can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-ofthe-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.

Infinite-Horizon Policy-Gradient Estimation <=SEP=> Gradient-based approaches to direct policy search in reinf orcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-f unction methods. In this paper we introduceGPOMDP, a simulation-based algorithm for generating a bi sedestimate of the gradient of theaverage rewardin Partially Observable Markov Decision Processes ( POMDPs) controlled by parameterized stochastic policies. A similar algorithm wa s proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm’s chief advantages are tha t i requires storage of only twice the number of policy parameters, uses one free parameter 2 [0; 1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowl edge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter is related to the mixing timeof the controlledPOMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control s paces, multiple-agents, higher-order derivatives, and a version for training stochastic policie s with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient e stimates generated by GPOMDP can be used in both a traditional stochastic gradient algori thm and a conjugate-gradient procedure to find local optima of the average reward.

Privacy-Preserving Public Auditing For Secure Cloud Storage <=SEP=> By using Cloud storage, users can access applications, services, software whenever they requires over the internet. Users can put their data remotely to cloud storage and get benefit of on-demand services and application from the resources. The cloud must have to ensure data integrity and security of data of user. The issue about cloud storage is integrity and privacy of data of user can arise. To maintain to overkill this issue here, we are giving public auditing process for cloud storage that users can make use of a third-party auditor (TPA) to check the integrity of data. Not only verification of data integrity, the proposed system also supports data dynamics. The work that has been done in this line lacks data dynamics and true public auditability. The auditing task monitors data modifications, insertions and deletions. The proposed system is capable of supporting public auditability, data dynamics and Multiple TPA are used for the auditing process. We also extend our concept to ring signatures in which HARS scheme is used. Merkle Hash Tree is used to improve block level authentication. Further we extend our result to enable the TPA to perform audits for multiple users simultaneously through Batch auditing.

Design Of Ternary Logic Gates Using CNTFET <=SEP=> This paper presents a novel design of ternary logic gates like STI,PTI,NTI,NAND and NOR using carbon nanotube field effect transistors. Ternary logic is a promising alternative to the conventional binary logic design technique, since it is possible to accomplish simplicity and energy efficiency in modern digital design due to the reduced circuit overhead such as interconnects and chip area. In this paper novel design of basic logic gates for ternary logic based on CNTFET, is proposed. Keywords— Carbon nano-tube Field Effect Transistor(CNTFET),MVL(multi valued logic),Ternary logic,STI,NTI,PTI

Sampled-Point Network for Classification of Deformed Building Element Point Clouds <=SEP=> Search-and-rescue (SAR) robots operating in post-disaster urban areas need to accurately identify physical site information to perform navigation, mapping and manipulation tasks. This can be achieved by acquiring a 3D point cloud of the environment and performing object recognition from the point cloud data. However, this task is complicated by the unstructured environments and potentially-deformed objects encountered during disaster relief operations. Current 3D object recognition methods rely on point cloud input acquired under suitable conditions and do not consider deformations such as outlier noise, bending and truncation. This work introduces a deep learning architecture for 3D class recognition from point clouds of deformed building elements. The classification network, consisting of stacked convolution and average pooling layers applied directly to point coordinates, was trained using point clouds sampled from a database of mesh models. The proposed method achieves robustness to input variability using point sorting, resampling, and rotation normalization techniques. Experimental results on synthetically-deformed object datasets show that the proposed method outperforms the conventional deep learning methods in terms of classification accuracy and computational efficiency.

Real-time 3 D Camera Tracking for Industrial Augmented Reality Applications <=SEP=> In this paper we present a new solution for real-time 3D camera pose estimation for Augmented Reality (AR) applications. The tracking system does not require special engineering of the environment, such as placing markers or beacons. The required input data are a CAD model of the target object to be tracked, and a calibrated reference image of it. We consider the whole process of camera tracking, and developed both an autonomous initialization and a real-time tracking procedure. The system is robust to abrupt camera motions, strong changes of the lighting conditions and partial occlusions. To avoid typical jitter and drift problems the tracker performs feature matching not only in an iterative manner, but also against stable reference features, which are dynamically cached in case of high confidence. We present experimental results generated with help of synthetic ground truth, real off-line and on-line image sequences using different types of target objects.

[Hyaluronic acid rheology: Basics and clinical applications in facial rejuvenation]. <=SEP=> Hyaluronic acid (HA) is the most widely used dermal filler to treat facial volume deficits and winkles specially for facial rejuvenation. Depending on various areas of the face, filler is exposed to two different forces (shear deformation and compression/stretching forces) resulting from intrinsec and external mechanical stress. The purpose of this technical note is to explain how rheology, which is the study of the flow and deformation of matter under strains, can be used in our clinical practice of facial volumization with fillers. Indeed, comprehension of the rheological properties of HA has become essential in selection of dermal filler targeted to the area of the face. Viscosity, elasticity and cohesivity are the main three properties to be taken into consideration in this selection. Aesthetic physicians and surgeons have to familiarize with those basics in order to select the HA with the right rheological properties to achieve a natural-looking and long-lasting outcome.

Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions <=SEP=> Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms.

Proof Analysis in Modal Logic <=SEP=> A general method for generating contractionand cut-free sequent calculi for a large family of normal modal logics is presented. The method covers all modal logics characterized by Kripke frames determined by universal or geometric properties and it can be extended to treat also Gödel–Löb provability logic. The calculi provide direct decision methods through terminating proof search. Syntactic proofs of modal undefinability results are obtained in the form of conservativity theorems.

Huffman Coding-Based Adaptive Spatial Modulation <=SEP=> Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, that can be utilized for data transmission via both signal space and spatial dimension. In this paper, we propose a Huffman coding-based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through the Huffman coding, i.e., designing variable length prefix codes, the transmit antennas can be activated with different probabilities. When the input signal is Gaussian distributed, the optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations of channel capacity. When the input is discrete QAM signal, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed adaptive transmission offers considerable performance improvement over the conventional spatial modulation and transmit antenna selection.

On Blockchain Applications : Hyperledger Fabric And Ethereum <=SEP=> Blockchain is a tamper-proof digital ledger which can be used to record public or private peer to peer network transactions and it cannot be altered retroactively without the alteration of all subsequent blocks of the network. A blockchain is updated via the consensus protocol that ensures a linear, unambiguous ordering of transactions. Blocks guarantee the integrity and consistency of the blockchain across a network of distributed nodes. Different blockchain applications use various consensus protocols for their working. Byzantine fault tolerance (BFT) is one of them and it is a characteristic of a system that tolerates the class of failures known as the Byzantine Generals Problem. Hyperledger, Stellar, and Ripple are three blockchain application which uses BFT consensus. The best variant of BFT is Practical Byzantine Fault tolerance (PBFT). Hyperledger fabric with deterministic transactions can run on the top of PBFT. This paper focuses on a survey of various consensus mechanisms and makes a comparative study of Hyperledger fabric and Ethereum. Keywords— consensus; hyperledger fabric; ethereum; byzantine fault tolerance;

MapFactory – Towards a mapping design pattern for big geospatial data <=SEP=> With big geospatial data emerging, cartographers and geographic information scientists have to find new ways of dealing with the volume, variety, velocity, and veracity (4Vs) of the data. This requires the development of tools that allow processing, filtering, analysing, and visualising of big data through multidisciplinary collaboration. In this paper, we present the MapFactory design pattern that will be used for the creation of different maps according to the (input) design specification for big geospatial data. The design specification is based on elements from ISO19115-1:2014 Geographic information -Metadata -Part 1: Fundamentals that would guide the design and development of the map or set of maps to be produced. The results of the exploratory research suggest that the MapFactory design pattern will help with software reuse and communication. The MapFactory design pattern will aid software developers to build the tools that are required to automate map making with big geospatial data. The resulting maps would assist cartographers and others to make sense of big geospatial data.

Exploiting Redundancy in Question Answering <=SEP=> Our goal is to automatically answer brief factual questions of the form ``When was the Battle of Hastings?'' or ``Who wrote The Wind in the Willows?''. Since the answer to nearly any such question can now be found somewhere on the Web, the problem reduces to finding potential answers in large volumes of data and validating their accuracy. We apply a method for arbitrary passage retrieval to the first half of the problem and demonstrate that answer redundancy can be used to address the second half. The success of our approach depends on the idea that the volume of available Web data is large enough to supply the answer to most factual questions multiple times and in multiple contexts. A query is generated from a question and this query is used to select short passages that may contain the answer from a large collection of Web data. These passages are analyzed to identify candidate answers. The frequency of these candidates within the passages is used to ``vote'' for the most likely answer. The approach is experimentally tested on questions taken from the TREC-9 question-answering test collection. As an additional demonstration, the approach is extended to answer multiple choice trivia questions of the form typically asked in trivia quizzes and television game shows.

VOS-GAN: Adversarial Learning of Visual-Temporal Dynamics for Unsupervised Dense Prediction in Videos <=SEP=> Recent GAN-based video generation approaches model videos as the combination of a time-independent scene component and a time-varying motion component, thus factorizing the generation problem into generating background and foreground separately. One of the main limitations of current approaches is that both factors are learned by mapping one source latent space to videos, which complicates the generation task as a single data point must be informative of both background and foreground content. In this paper we propose a GAN framework for video generation that, instead, employs two latent spaces in order to structure the generative process in a more natural way: 1) a latent space to generate the static visual content of a scene (background), which remains the same for the whole video, and 2) a latent space where motion is encoded as a trajectory between sampled points and whose dynamics are modeled through an RNN encoder (jointly trained with the generator and the discriminator) and then mapped by the generator to visual objects’ motion. Additionally, we extend current video discrimination approaches by incorporating in the learning procedure motion estimation and, leveraging the peculiarity of the generation process, unsupervised pixel-wise dense predictions. Extensive performance evaluation showed that our approach is able to a) synthesize more realistic videos than state-of-the-art methods, b) learn effectively both local and global video dynamics, as demonstrated by the results achieved on a video action recognition task over the UCF-101 dataset, and c) accurately perform unsupervised video object segmentation on standard video benchmarks, such as DAVIS, SegTrack and F4K-Fish.

Pharmacist-managed medication therapy adherence clinics: The Malaysian experience. <=SEP=> In Malaysia, the first pharmacist-managed clinic was established in 2004. This type of clinics is locally known as a medication therapy adherence clinic (MTAC).1e3 In fact, these clinics were introduced by the Pharmaceutical Services Division (PSD), Ministry of Health Malaysia (MOH) as part of the clinical pharmacy services to improve the quality, safety, and cost-effectiveness of patient care through better medicines management at ambulatory care settings in hospitals and MOH clinics.2 The major aims of the clinics include optimization of medication therapy, improvement of medication adherence, and prevention or reduction of adverse drug events and other medication related problems.1 Over a decade, the number of MTACs has increased tremendously to 660 by the end of 2013.1 Moreover, currently, there are 13 types of clinics being operated in Malaysia that include neurology (stroke), diabetes, heart failure, respiratory, psoriasis, anticoagulation, rheumatology, nephrology, hepatitis, geriatrics, retroviral disease, hemophilia, and psychiatry.1,3,4 The pharmacists operate the clinics and provide pharmaceutical care services to the patients in collaboration with physicians and other healthcare professionals according to specific protocols. The specific roles of the pharmacist include providing drug information, patient education and counseling, identifying and solving drugrelated problems, monitoring patients' drug therapy and treatment outcomes, offering feedback on patient progress, making appropriate recommendations to physicians to individualize the patients' drug regimens according to patient-related factors including clinical, humanistic, and economic factors.1e4 Several studies have been conducted to evaluate the impact of pharmacist-managed clinics on patient care in Malaysia.5e10 These studies have demonstrated that the clinics are beneficial in optimizing medication therapy, improving medication adherence, and giving better clinical outcomes. Moreover, some studies have shown that these clinics are more cost-effective than standard medical care.11,12 In addition, it has been shown that the patients are satisfied with the pharmaceutical care services provided by these clinics.13,14 Therefore, the current evidence supports further expansion of these clinics in the ambulatory care system in Malaysia. We believe there are several factors that have led to the success of the pharmacist-managed clinics in Malaysia. These include the tremendous support and guidance from the PSD, Ministry of Health Malaysia to help in the establishment of these clinics. As an example of this support, the PSD has established a protocol for each clinic to help the pharmacists run the clinic, and to enable standardization of practice and the expansion of the clinics throughout the country. These protocols are written by specialized clinical pharmacy committees (e.g. respiratory subspecialty,

An Intelligent RFID Reader and its Application in Airport Baggage Handling System <=SEP=> In civil aviation baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance, so as to improve the management efficiency and the users' satisfaction. An intelligent RFID reader which has the ability of data disposal and provides EPC Edge Savant services is presented. The prototype readers and its experiment in the airport baggage handling system are also introduced. The workflow of the reader can be configured dynamically, which makes the deployment much more flexible. Real-time tasks can also be assigned to implement edge business process by the intelligent reader.

Realtime Ray Tracing on GPU with BVH-based Packet Traversal <=SEP=> Recent GPU ray tracers can already achieve performance competitive to that of their CPU counterparts. Nevertheless, these systems can not yet fully exploit the capabilities of modern GPUs and can only handle medium-sized, static scenes. In this paper we present a BVH-based GPU ray tracer with a parallel packet traversal algorithm using a shared stack. We also present a fast, CPU-based BVH construction algorithm which very accurately approximates the surface area heuristic using streamed binning while still being one order of magnitude faster than previously published results. Furthermore, using a BVH allows us to push the size limit of supported scenes on the GPU: We can now ray trace the 12.7 million triangle Power Plant at 1024 times 1024 image resolution with 3 fps, including shading and shadows.

Mobile Forensic Data Analysis: Suspicious Pattern Detection in Mobile Evidence <=SEP=> Culprits’ identification by the means of suspicious pattern detection techniques from mobile device data is one of the most important aims of the mobile forensic data analysis. When criminal activities are related to entirely automated procedures such as malware propagation, predicting the corresponding behavior is a rather achievable task. However, when human behavior is involved, such as in cases of traditional crimes, prediction and detection become more compelling. This paper introduces a combined criminal profiling and suspicious pattern detection methodology for two criminal activities with moderate to the heavy involvement of mobile devices, cyberbullying and low-level drug dealing. Neural and Neurofuzzy techniques are applied on a hybrid original and simulated dataset. The respective performance results are measured and presented, the optimal technique is selected, and the scenarios are re-run on an actual dataset for additional testing and verification.

A GPU-based implementation of motion detection from a moving platform <=SEP=> We describe a GPU-based implementation of motion detection from a moving platform. Motion detection from a moving platform is inherently difficult as the moving camera induces 2D motion field in the entire image. A step compensating for camera motion is required prior to estimating of the background model. Due to inevitable registration errors, the background model is estimated according to a sliding window of frames to avoid the case where erroneous registration influences the quality of the detection for the whole sequence. However, this approach involves several characteristics that put a heavy burden on real-time CPU implementation. We exploit GPU to achieve significant acceleration over standard CPU implementations. Our GPU-based implementation can build the background model and detect motion regions at around 18 fps on 320times240 videos that are captured for a moving camera.

How to do a grounded theory study: a worked example of a study of dental practices <=SEP=> BACKGROUND
Qualitative methodologies are increasingly popular in medical research. Grounded theory is the methodology most-often cited by authors of qualitative studies in medicine, but it has been suggested that many 'grounded theory' studies are not concordant with the methodology. In this paper we provide a worked example of a grounded theory project. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature.


METHODS
We documented a worked example of using grounded theory methodology in practice.


RESULTS
We describe our sampling, data collection, data analysis and interpretation. We explain how these steps were consistent with grounded theory methodology, and show how they related to one another. Grounded theory methodology assisted us to develop a detailed model of the process of adapting preventive protocols into dental practice, and to analyse variation in this process in different dental practices.


CONCLUSIONS
By employing grounded theory methodology rigorously, medical researchers can better design and justify their methods, and produce high-quality findings that will be more useful to patients, professionals and the research community.

qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data <=SEP=> Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.

A Comparison of String Distance Metrics for Name-Matching Tasks <=SEP=> Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. Overall, the best-performing method is a hybrid scheme combining a TFIDF weighting scheme, which is widely used in information retrieval, with the Jaro-Winkler string-distance scheme, which was developed in the probabilistic record linkage community.

The power of statistical tests in meta-analysis. <=SEP=> Calculations of the power of statistical tests are important in planning research studies (including meta-analyses) and in interpreting situations in which a result has not proven to be statistically significant. The authors describe procedures to compute statistical power of fixed- and random-effects tests of the mean effect size, tests for heterogeneity (or variation) of effect size parameters across studies, and tests for contrasts among effect sizes of different studies. Examples are given using 2 published meta-analyses. The examples illustrate that statistical power is not always high in meta-analysis.

Frequent Pattern Compression: A Significance-Based Compression Scheme for L2 Caches <=SEP=> With the widening gap between processor and memory speeds, memory system designers ma find cache compression beneficial to increase cache capacity and reduce off-chip bandwidth Most hardware compression algorithms fall into the dictionary-based category, which depend on building a dictionary and using its entries to encode repeated data values. Such algorithms are effective in compressing large data blocks and files. Cache lines, however, are typically short (32-256 bytes), and a per-line dictionary places a significant overhead that limits the compressibility and increases decompression latency of such algorithms. For such short lines significance-based compression is an appealing alternative. We propose and evaluate a simple significance-based compression scheme that has a low com pression and decompression overhead. This scheme, Frequent Pattern Compression (FPC compresses individual cache lines on a word-by-word basis by storing common word patterns in a compressed format accompanied with an appropriate prefix. For a 64-byte cache line, compression can be completed in three cycles and decompression in five cycles, assuming 1 FO4 gate delays per cycle. We propose a compressed cache design in which data is stored in compressed form in the L2 caches, but are uncompressed in the L1 caches. L2 cache lines ar compressed to predetermined sizes that never exceed their original size to reduce decompre sion overhead. This simple scheme provides comparable compression ratios to more comple schemes that have higher cache hit latencies.

A pre-collision control strategy for human-robot interaction based on dissipated energy in potential inelastic impacts <=SEP=> Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a potential inelastic impact. This quantity represents the fracture energy lost when a human-robot collision occurs, modeling both clamped and unclamped cases. It depends on the robot reflected mass and velocity in the impact direction. The proposed index is expressed in analytical form suitable to be integrated in a constraint-based pre-collision control strategy. The exploited control architecture allows to perform a given robot task while simultaneously bounding our injury assessment and minimizing the reflected mass in the direction of the impact. Experiments have been performed on a lightweight robot ABB FRIDA to validate the proposed injury index as well as the pre-collision control strategy.

A Quadratic-Complexity Observability-Constrained Unscented Kalman Filter for SLAM <=SEP=> This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: the cubic computational complexity in the number of states and the inconsistency of the state estimates. To address the first issue, we introduce a new sampling strategy for the UKF, which has constant computational complexity. As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF)-based SLAM, i.e., quadratic in the size of the state vector. Furthermore, we investigate the inconsistency issue by analyzing the observability properties of the linear-regression-based model employed by the UKF. Based on this analysis, we propose a new algorithm, termed observability-constrained (OC)-UKF, which ensures the unobservable subspace of the UKF's linear-regression-based system model is of the same dimension as that of the nonlinear SLAM system. This results in substantial improvement in the accuracy and consistency of the state estimates. The superior performance of the OC-UKF over other state-of-the-art SLAM algorithms is validated by both Monte-Carlo simulations and real-world experiments.

Routing protocols in Vehicular Delay Tolerant Networks: A comprehensive survey <=SEP=> This article presents a comprehensive survey of routing protocols proposed for routing in Vehicular Delay Tolerant Networks (VDTN) in vehicular environment. DTNs are utilized in various operational environments, including those subject to disruption and disconnection and those with high-delay, such as Vehicular Ad-Hoc Networks (VANET). We focus on a special type of VANET, where the vehicular traffic is sparse and direct end-to-end paths between communicating parties do not always exist. Thus, communication in this context falls into the category of Vehicular Delay Tolerant Network (VDTN). Due to the limited transmission range of an RSU (Road Side Unit), remote vehicles, in VDTN, may not connect to the RSU directly and thus have to rely on intermediate vehicles to relay the packets. During the message relay process, complete end-to-end paths may not exist in highly partitioned VANETs. Therefore, the intermediate vehicles must buffer and forward messages opportunistically. Through buffer, carry and forward, the message can eventually be delivered to the destination even if an end-to-end connection never exists between source and destination. The main objective of routing protocols in DTN is to maximize the probability of delivery to the destination while minimizing the end-to-end delay. Also, vehicular traffic models are important for DTN routing in vehicle networks because the performance of DTN routing protocols is closely related to population and mobility models of the network. 2014 Elsevier B.V. All rights reserved.