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In this paper, because of infinite-dimensional feature and complex nonlinearities of the distributed parameter systems, a new data-driven modeling method has been proposed. The temporal-spatial output of the system is measured at a finite number of spatial locations. At the same time it is assumed that the input of the system is a temporal variable. Firstly, Karhunen-Loève(KL) decomposition is used...
Density peak (DP) based clustering algorithm is a recently proposed clustering approach and has been shown to be with great potential. This algorithm is based on the simple assumption that cluster centers have high local density and they are relatively far from each other. This observation is used to isolate cluster centers from other data. By making use of the density relationship among neighboring...
This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based techniques. Based on an analysis of traditional and more recent learning-based methods, we generalize existing discriminative approaches by using more powerful regularization, based...
We present an approach for blind image deblurring, which handles non-uniform blurs. Our algorithm has two main components: (i) A new method for recovering the unknown blur-field directly from the blurry image, and (ii) A method for deblurring the image given the recovered non-uniform blur-field. Our blur-field estimation is based on analyzing the spectral content of blurry image patches by Re-blurring...
We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain...
Blob detection and image denoising are fundamental, and sometimes related, tasks in computer vision. In this paper, we propose a blob reconstruction method using scale-invariant normalized unilateral second order Gaussian kernels. Unlike other blob detection methods, our method suppresses non-blob structures while also identifying blob parameters, i.e., position, prominence and scale, thereby facilitating...
One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown...
Considering the problems of low recognition rate and poor robustness in traditional recognition algorithms, we propose a license plate character recognition algorithm based on convolution neural network. In this paper, we adopt a coarse-to-fine strategy for designing the network architecture. Through the convolutional layers and pooling layers, features of input images will be extracted and then sent...
The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the part interactions are complex and of higher-order. To address these issues, we...
The main contribution of this paper is the derivation of theoretical relationships between the impairments in an I/Q modulator and their impact on the structure of the kernels of a Volterra behavioral model. The analysis is focused on the imperfections produced by I/Q gain imbalance and quadrature error and shows how the model coefficients depend on the impairment strength. Closed-form expressions...
The long-tail phenomenon tells us that there are many items in the tail. However, not all tail items are the same. Each item acquires different kinds of users. Some items are loved by the general public, while some items are consumed by eccentric fans. In this paper, we propose a novel metric, item eccentricity, to incorporate this difference between consumers of the items. Eccentric items are defined...
Software often requires frequent updates to improve performance and reliability. Typically, a general update process is performed after terminating a program although this is not applicable to applications that require non-disruptive services such as networks and satellites. In order to address this issue, network service providers often provide a technology termed as an in-service software upgrade...
Focused images captured by the lens system suffer image degradation due to factors, such as aberration, caused by the optical structure. In the simple lens system, aberration is more severe because of the simplification of the imaging system. In the existing imaging model, the blur kernel of the image is usually described by the point spread function. A few studies have shown that the blur kernel...
With the bottom-line goal of increasing the throughput of a GPU-accelerated JPEG 2000 encoder, this paper evaluates whether the post-compression rate control and packetization routines should be carried out on the CPU or on the GPU. Three co-processing models that differ in how the workload is split among the CPU and GPU are introduced. Both routines are discussed and algorithms for executing them...
Network virtualization offers flexibility by decoupling virtual network from the underlying physical network. Software-Defined Network (SDN) could utilize the virtual network. For example, in Software-Defined Networks, the entire network can be run on commodity hardware and operating systems that use virtual elements. However, this could present new challenges of data plane performance. In this paper,...
Reverse engineering binary code is notoriously difficult and, especially, understanding a binary's dynamic data structures. Existing data structure analyzers are limited wrt. program comprehension: they do not detect complex structures such as skip lists, or lists running through nodes of different types such as in the Linux kernel's cyclic doubly-linked list. They also do not reveal complex parent-child...
A Bayesian optimization technique enables a short search time for a complex prediction model that includes many hyperparameters while maintaining the accuracy of the prediction model. Here, we apply a Bayesian optimization technique to the drug-target interaction (DTI) prediction problem as a method for computational drug discovery. We target neighborhood regularized logistic matrix factorization...
Brain tumour diagnosis is usually a vital use of medical image processing, where clustering technique commonly used with medical application especially regarding brain tumour diagnosis with magnetic resonance imaging (MRI). In this MRI has been considered because it provides accurate visualization of anatomical structure of tissues. The conventional mean shift technique utilizes radially symmetric...
Network performance is one of the most important entities in today’s long-distance networks. TCP congestion control mechanisms play an important role in these networks. Most of the current TCP congestion control mechanisms which are also known as TCP variants, detect congestion and slow down the packets transmission to avoid further congestion in the network. In this paper, three classes...
Stream processing applications have high-demanding performance requirements that are hard to tackle using traditional parallel models on modern many-core architectures, such as GPUs. On the other hand, recent dataflow computing models can naturally exploit parallelism for a wide class of applications. This work presents an extension to an existing dataflow library for Java. The library extension implements...
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