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To satisfy growing computational demands of modern applications, significant enhancements have been introduced in the contemporary processor architectures with the aim to increase their attainable performance, such as increased number of cores, improved capability of memory subsystem and enhancements in the processor pipeline [1]. Therefore, the performance improvements are usually coupled with an...
Fractal coding has been proved useful for image compression, and it is also proved effective for image retrieval. In the paper, we present a statistical method called variable bandwidth kernel density estimation to analyze fractal coding parameters. Then retrieve images using the retrieval parameters constructed with this method. Experimental results show that the proposed method with a variable optimized...
A solution for the traditional Bayesian classification problem in non-traditional conditions is proposed, when the distributions and a priori probabilities of classes are unknown, but a trained sample from the zero class (labeled positive) and mixed sample (unlabeled) are available. Mixed sample will be employed in the learning to restore mixed distribution and as a test sample for constructed classifier...
We propose a new notion of variable bandwidth and develop the basic theory. Starting with a strictly positive function p on ℝ (a bandwidth parametrization), a function is of variable bandwidth Ω, if it is contained in the spectral subspace of the elliptic operator Apƒ = − d/dx (p(x) d/dx) ƒ with spectrum [0, Ω]. We derive (i) (nonuniform) sampling theorems and corresponding algorithms, and (ii) necessary...
In flexible functional split, functions of a virtualized evolved NodeB (eNB) can be disaggregated in distributed computational resources. One of the main constraints for their placement is the latency experienced by the communication between the Virtual Machines (VM) hosting the functions. This paper evaluates experimentally the latency limits for different functional splits providing insights on...
Efficient test and diagnosis methods are required to ensure high levels of dependability of the electronic systems deployed to the market. These methods involve a trade-off in terms of accessibility to test nodes, test stimuli complexity, area overhead, and data processing that, altogether determine the impact that the involved operations have in the final cost, performance, and reliability presented...
Many today's real-time applications, such as Advanced Driver Assistant Systems (ADAS), demand both high computing power and safety guarantees. High computing power can be easily delivered by, now ubiquitous, multi-core CPUs or by a heterogeneous system with a multi-core CPU and a parallel accelerator such as a GPU. Reaching the required safety level in such a system is by far more difficult because...
Estimating expected polynomials of density functions from samples is a basic problem with numerous applications in statistics and information theory. Although kernel density estimators are widely used in practice for such functional estimation problems, practitioners are left on their own to choose an appropriate bandwidth for each application in hand. Further, kernel density estimators suffer from...
Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of data boundary. For example, it is observed that with Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary...
More and more Advanced Driver Assistance Systems (ADAS) are entering the market for improving both safety and comfort by assisting the driver with their driving task. An important aspect in developing future ADAS and Automated Driving Systems (ADS) is testing and validation. Validating the failure rate of an ADS requires so many operational hours that testing in real time is almost impossible. One...
Live migration of virtual machines (VM) is one of the key characteristics of virtualization for load balancing, system maintenance, power management, etc., in data centers or clusters. In order to reduce the data transferred and shorten the migration time, the compression techniques have been widely used to accelerate VM migration. However, different compression approaches have different compression...
Nowadays, high-bandwidth networks are easily accessible in data centers. However, existing distributed graph-processing frameworks fail to efficiently utilize the additional bandwidth capacity in these networks for higher performance, due to their inefficient computation and communication models, leading to very long waiting times experienced by users for the graph-computing results. The root cause...
Internet of things wireless networking with long-range, low power and low throughput is raising as a new paradigm enabling to connect trillions of devices efficiently. In such networks with low power and bandwidth devices, localization becomes more challenging. In this work we take a closer look at the underlying aspects of received signal strength indicator (RSSI) based localization in UNB long-range...
In this paper we propose a low-overhead optimizer for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel on the Intel Xeon Phi manycore processor. The architectural differences of such processors compared to their multicore counterparts overly expose inherent structural weaknesses of different sparse matrices, intensifying performance issues beyond the traditionally reported memory bandwidth...
Ocean remote sensing based satellite image is useful for the Earth observation such as altimetry, Significant Wave Height, and wind speed measurement. However, The Global Navigation Satellite System (GNSS) represents the new challenge using special feature of the reflected signal to observe characteristics of the ocean call GNSS - reflectometry. The advantages of this technique are that using the...
Nowadays high performance computers (HPC) are used to solve increasingly complex problems and process larger amounts of data. The growing computational requirements of applications can be met by utilizing more compute nodes. However, the average I/O performance a compute node can utilize is reduced with increased number of nodes. The performance gap between computation and I/O has long been a primary...
Simulation is a fast, controlled, and reproducible way to evaluate new algorithms for distributed computing platforms in a variety of conditions. However, the realism of simulations is rarely assessed, which critically questions the applicability of a whole range of findings. In this paper, we present our efforts to build platform models from application traces, to allow for the accurate simulation...
This work is focused on monthly electricity demand prediction, which is necessary for the maintenance planning in power systems as well as for negotiation forward contracts. In the proposed approach patterns of the load time series are defined, which unify input and output data and filter out the trend. Relationships between inputs and outputs simplified due to patterns are modeled using nonparametric...
The convolutional neural network (CNN) is a state-of-the-art model that can achieve significantly high accuracy in many machine-learning tasks. Recently, for further developing the practical applications of CNNs, efficient hardware platforms for accelerating CNN have been throughly studied. A binarized neural network has been reported to minimize the multipliers, which consume a large amount of resources,...
Clustering is one of the most important unsupervised classification strategies in data analysis. In this sense, a new clustering approach proposed a fast search algorithm of cluster centers based on their local densities has taken place. In the present paper, we suggest a new performed approach that combine the estimation of the local density and the use of the entropy. So the clustering algorithm...
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