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The classical stereophonic acoustic echo cancellation (SAEC) scheme requires the identification of four echo paths using the same number of adaptive algorithms. The SAEC setup can be reinterpreted by employing the widely linear (WL) model, which recasts the two-input/two-output system with real random variables as a single-input/single-output system with complex random variables. The WL model improves...
The Internet of Things (IoT) is announced to swamp the world. In order to understand the emergent behaviour of connected things, effective support for the modelling of connection and failure probabilities, execution and waiting times, as well as resource consumptions of various kinds is needed. At the heart of IoT are flexible and adaptive communication and interaction patterns between things, meant...
As Cyber-Physical Systems (CPS) operate in open, dynamic and diverse environments, they need to be self-adaptive to address the uncertainty challenges. And it is urgent to study self-adaptive software intensive Cyber-Physical Systems (self-adaptive CPS). As self-adaptive CPS interact closely with the environments and users, their requirements analysis is particularly challenging. General requirement...
This paper deals with the problem of properly simulating the Internet of Things (loT). Simulating an loT allows evaluating strategies that can be employed to deploy smart services over different kinds of territories. However, the heterogeneity of scenarios seriously complicates this task. This imposes the use of sophisticated modeling and simulation techniques. We discuss novel approaches for the...
Force computations are one of the most time consuming part in performing Molecular Dynamics (MD) simulations. Adaptively Restrained Molecular Dynamics (ARMD) makes it possible to perform fewer force calculations by adaptively restraining particles positions. This paper introduces parallel algorithms for single-pass incremental force computations to take advantage of adaptive restraints using the Message...
In this study' a model selection procedure for varying-coefficient model based on longitudinal data is proposed to distinguish three types of variables: variables not in the model' variables in the model with time-independent coefficients and variables in the model with time-varying coefficients. To identify these three kinds of variables simultaneously' we extend the present variable selection method...
The spatial information of the video sequence is introduced into the background modeling process to deal with the problems of the traditional single-pixel based mixture of Gaussians moving objects detection method. Gaussian modeling process is improved to adaptively select the number of models, learning rate and other parameters by adjacent neighborhood pixels updating, spatio-temporal smoothing and...
This paper presents an application of Model Reference Adaptive Control (MRAC) method based on MIT rule for closed loop fractional-order control systems. The main advantage of the method is that it may gain adaptation capability for existing fractional-order systems. It essentially contains two loops, which are the inner loop for fractional-order PID (FOPID) control of plant, and the outer loop for...
This paper considers the problem of state estimations in virus/ worm epidemic dynamic system with time-dependent parameters in arbitrary sparse networks by using continuous-discrete Extended Kalman Filter (so-called Hybrid Extended Kalman Filter [1]). The virus spreading dynamic model has unmeasurable states and with highly nonlinearities which makes the state estimation complicated and not straightforward...
CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary...
The interpolation of correspondences (EpicFlow) was widely used for optical flow estimation in most-recent works. It has the advantage of edge-preserving and efficiency. However, it is vulnerable to input matching noise, which is inevitable in modern matching techniques. In this paper, we present a Robust Interpolation method of Correspondences (called RicFlow) to overcome the weakness. First, the...
Traditional matrix factorization methods approximate high dimensional data with a low dimensional subspace. This imposes constraints on the matrix elements which allow for estimation of missing entries. A lower rank provides stronger constraints and makes estimation of the missing entries less ambiguous at the cost of measurement fit. In this paper we propose a new factorization model that further...
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation...
The current network architecture is built on a host-centric communication model that is suitable for early network information transmission needs. However, with the emergence and rapid growth of network applications and services, the adaptability, flexibility, scalability and other defects have become increasingly prominent, and the current network architecture can't dynamically provide the required...
We propose a novel deep layer cascade (LC) method to improve the accuracy and speed of semantic segmentation. Unlike the conventional model cascade (MC) that is composed of multiple independent models, LC treats a single deep model as a cascade of several sub-models. Earlier sub-models are trained to handle easy and confident regions, and they progressively feed-forward harder regions to the next...
In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-based domain adaptation methods generally ignore the changes of class prior distributions, i.e., class weight bias across domains. This remains an open problem but ubiquitous for domain adaptation, which can be caused by...
For the problem of sail-assisted ship prone to track deviation influenced by wind and other disturbances, a fuzzy adaptive iterative sliding mode control method is presented. The evaluation function is introduced into fuzzy control scheme to evaluate and adjust the designed parameters online. Under the proposed method, the estimation of the uncertain parameters and disturbances can be avoided, moreover,...
We present a rumor propagation model considering rumor acceptability function and simulate rumor propagation on complex networks with Repast simulation platform in this paper. We introduce the structure and the main class library of Repast simulation platform. Then we build an efficient model based on the multi-agent modeling method. Through the model, we simulate the behavior of rumor propagation...
Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as the and of. Other words that may seem visual can often be predicted reliably just from the language model e...
Developing instructional design for e-content and checking its effectiveness among a group of learners is very significant in e-learning. In the current educational system, teachers are giving instructions or delivering learning contents to learners, without understanding the learner profile parameters such as learning style, motivation, attitude, aptitude etc. In an e-learning environment, providing...
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