Description:
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
Description:
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
Description:
High Efficiency Video Coding (HEVC) could not provide real time facilities to the limited processing and battery powered electronic devices as its encoding time complexity increases multiple times compared to its predecessor. Numerous researchers contribute to address this limitation by reducing a number of motion estimation (ME) modes where they analyze homogeneity, residual and statistical correlation among different modes. Although their approaches save some encoding time, however, could not reach the similar rate-distortion (RD) performance with HEVC encoder as they merely depend on existing Lagrangian cost function (LCF) within HEVC framework. To overcome this limitation, in this paper, we capture visual attentive Foreground motion and salient region (FMSR) which are sensitive to human visual system for quality assessment. The FMSR features captured by visual attentive and dynamic background modeling are adaptively synthesized to determine a subset of candidate modes. This preprocessing phase is independent from LCF. Since the proposed technique can avoid exhaustive exploration of all modes with simple criteria, it can reduce 27% encoding time on average. With efficient selection of FMSR-based appropriate block partitioning modes, it can also improve up to 1.0dB peak signal-to-noise ratio (PSNR).
Description:
A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.
Description:
Clusters may exist in different subspaces of a multidimensional dataset. Traditional full-space clustering algorithms have difficulty in identifying these clusters. Various subspace clustering algorithms have used different subspace search strategies. They require clustering to assess whether cluster(s) exist in a subspace. In addition, all of them perform clustering by measuring similarity between points in the given feature space. As a result, the subspace selection and clustering processes are tightly coupled. In this paper, we propose a new subspace clustering framework named CSSub (Clustering by Shared Subspaces). It enables neighbouring core points to be clustered based on the number of subspaces they share. It explicitly splits candidate subspace selection and clustering into two separate processes, enabling different types of cluster definitions to be employed easily. Through extensive experiments on synthetic and real-world datasets, we demonstrate that CSSub discovers non-redundant subspace clusters with arbitrary shapes in noisy data; and it significantly outperforms existing state-of-the-art subspace clustering algorithms.
Description:
Background and objectives: Recently, there have been calls for RFA to be implemented in the bipolar mode for cancer treatment due to the benefits it offers over the monopolar mode. These include the ability to prevent skin burns at the grounding pad and to avoid tumour track seeding. The usage of bipolar RFA in clinical practice remains uncommon however, as not many research studies have been carried out on bipolar RFA. As such, there is still uncertainty in understanding the effects of the different RF probe configurations on the treatment outcome of RFA. This paper demonstrates that the electrode lengths have a strong influence on the mechanics of bipolar RFA. The information obtained here may lead to further optimization of the system for subsequent uses in the hospitals. Methods: A 2D model in the axisymmetric coordinates was developed to simulate the electro-thermophysiological responses of the tissue during a single probe bipolar RFA. Two different probe configurations were considered, namely the configuration where the active electrode is longer than the ground and the configuration where the ground electrode is longer than the active. The mathematical model was first verified with an existing experimental study found in the literature. Results: Results from the simulations showed that heating is confined only to the region around the shorter electrode, regardless of whether the shorter electrode is the active or the ground. Consequently, thermal coagulation also occurs in the region surrounding the shorter electrode. This opened up the possibility for a better customized treatment through the development of RF probes with adjustable electrode lengths. Conclusions: The electrode length was found to play a significant role on the outcome of single probe bipolar RFA. In particular, the length of the shorter electrode becomes the limiting factor that influences the mechanics of single probe bipolar RFA. Results from this study can be used to further develop and optimize bipolar RFA as an effective and reliable cancer treatment technique. (C) 2019 Elsevier B.V. All rights reserved.
Description:
In many real-world clustering problems, there usually exist little information about the clusters underlying a certain dataset. For example, the number of clusters hidden in many datasets is usually not known a priori. This is an issue because many traditional clustering methods require such information as input. This paper examines a practical stochastic clustering method (PSCM) that has the ability to find clusters in datasets without requiring users to specify the centroids or the number of clusters. By comparing with traditional methods (k-means, self-organising map and hierarchical clustering methods), the performance of PSCM is found to be robust against overlapping clusters and clusters with uneven sizes. The proposed method also scales well with datasets having varying number of clusters and dimensions. Finally, our experimental results on real-world data confirm that the proposed method performs competitively against the traditional clustering methods in terms of clustering accuracy and efficiency.
Description:
In this paper, we present an automated color transfer based video fusion method to attain real-time color night vision capability for night-time video surveillance. We utilize simple RGB Color transfer technique to fused pseudo colored video frames without conversion to any uncorrelated color space. We investigated that final color fusion results greatly depend on the selection of target color Image. Therefore, rather than using any arbitrary target color image based on mere general visual anticipation, we have automated target color image selection using structural similarity and color saturation. We further apply color enhancement to improve final appearance of color fused images. Subjective and objective quality evaluations greatly indicate the effectiveness of our color video fusion method for nighttime video surveillance applications.
Description:
This paper describes the MODDE (Model of Decision support system Design and Evaluation) framework in some detail. The work is in progress and is being currently applied to the EMBRACE project being developed for the Refugee Review Tribunal (RRT) of Australia. Refugee law is the general legal area we are working in, while the specific domain under investigation is that of the decision makers at the RRT. EMBRACE is a decision support system being designed to assist the RRT in maintaining consistency of decisions, and preserve discretion of decision makers as well as making it easier to cope with high volumes of work in decreasing time frames. The use of the MODDE framework is intended to facilitate systematic attention to important features of decision making in our specific legal domain and to provide a sound basis upon which to evaluate a part of the system intrinsic to user acceptance. Copyright 2001 ACM.
Description:
The task of classifying the genre of polyphonic music signals is traditionally done using only low level features of the signal. In this paper high level features have been applied to improve the task of music genre classification. The use of statistical chord features and chord progression information in conjunction with low level features are proposed in this paper. The chord progression information is manifested in genre probability descriptors calculated using a pattern matching algorithm. Our proposed method provides an improvement of 12.4% in the classification results over a commonly compared technique.
Description:
In this paper, we present a novel approach of implementing a combination methodology to find appropriate neural network architecture and weights using an evolutionary least square based algorithm (GALS).1 This paper focuses on aspects such as the heuristics of updating weights using an evolutionary least square based algorithm, finding the number of hidden neurons for a two layer feed forward neural network, the stopping criterion for the algorithm and finally some comparisons of the results with other existing methods for searching optimal or near optimal solution in the multidimensional complex search space comprising the architecture and the weight variables. We explain how the weight updating algorithm using evolutionary least square based approach can be combined with the growing architecture model to find the optimum number of hidden neurons. We also discuss the issues of finding a probabilistic solution space as a starting point for the least square method and address the problems involving fitness breaking. We apply the proposed approach to XOR problem, 10 bit odd parity problem and many real-world benchmark data sets such as handwriting data set from CEDAR, breast cancer and heart disease data sets from UCI ML repository. The comparative results based on classification accuracy and the time complexity are discussed.
Description:
Solutions for deriving the most consistent Bayesian gene regulatory network model from given data sets using evolutionary algorithms typically only result in locally optimal solutions.
Description:
This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds local models instead of global models. Feating is a generic ensemble approach that can enhance the predictive performance of both stable and unstable learners. In contrast, most existing ensemble approaches can improve the predictive performance of unstable learners only. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble through an increased level of localisation in Feating. Our empirical evaluation shows that Feating performs significantly better than Boosting, Random Subspace and Bagging in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by Feating makes feasible SVM ensembles that would otherwise be infeasible for large data sets. When SVM is the preferred base learner, we show that Feating SVM performs better than Boosting decision trees and Random Forests. We further demonstrate that Feating also substantially reduces the error of another stable learner, k-nearest neighbour, and an unstable learner, decision tree.
Description:
Theodolites represent a well-established three-dimensional-point-measuring technology. However, when used for robot applications they have to be properly calibrated to fulfil the necessary accuracy requirements. The theodolite calibration methods reported in the literature involve the use of costly sophisticated equipment not easily available to most users. Therefore, a new simplified calibration technique is presented based on the use of a graduated precision bar suspended freely to align with the vertical direction. To develop efficient mathematical models, the theodolites will be regarded as 2R open-ended mechanisms with the end-effector axis directed along the line of sight. The proposed models are then coded in a computer program designed to verify the validity of the technique presented. The simulation results will be presented at the end of the paper.
Description:
Theodolites represent a well-established three-dimensional-point-measuring technology. However, when used for robot applications they have to be properly calibrated to fulfil the necessary accuracy requirements. The theodolite calibration methods reported in the literature involve the use of costly sophisticated equipment not easily available to most users. Therefore, a new simplified calibration technique is presented based on the use of a graduated precision bar suspended freely to align with the vertical direction. To develop efficient mathematical models, the theodolites will be regarded as 2R open-ended mechanisms with the end-effector axis directed along the line of sight. The proposed models are then coded in a computer program designed to verify the validity of the technique presented. The simulation results will be presented at the end of the paper.