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This paper shows the relationship between switching/sampling frequency, samples number and sine wave fundamental frequency with the intention of giving a basis to understand how the algorithm was designed. It also punctuates mathematical and implementation constraints considered in the optimization algorithm design. Similarly it explains the numerical methods and procedures used in the algorithm to...
The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
Machine learning based classifiers used quite often for predicting forest cover types, are the Naïve Bayes classifier, the k-Nearest Neighbors classifier, and the Random forest classifier. This paper is directed towards examining all of these classifiers coupled with feature selection and attribute derivation in order to evaluate which one is best suited for forest cover type classification. Numerous...
In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti...
In software projects, there is a data repository which contains the bug reports. These bugs are required to carefully analyse and resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the deleying in addressing some important bugs resolutions. To overcome this problem, researchers have introduced many techniques. One of the commonly used algorithm...
Studies focused on soundscape are important on biological conservation, because natural sounds are permanent and with dynamic properties, they have been linked to the welfare of the environment and the structure of the landscape. These studies usually analyze the sound in time and frequency domains, with computationally heavy and centralized algorithms. However, new technologies for real time analysis...
The use of multisource data in remote sensing image classification has become increasingly popular. Although additional features incorporated could improve classification accuracy, the amount of relevant information may induce interclass confusion. Feature selection plays an important role in image analysis process. This study investigates feature space optimization in the use of multispectral UltraCAM...
Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes of the terrestrial surface. Here, a temporal-spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated MODIS13Q1 pixels based on reliable MODIS13Q1 data. NDVIs of contaminated pixels were firstly computed through linear interpolation of adjacent high-quality...
Flood is the most frequent disaster in the world, which can do harm to agriculture and threat to food security. Using kernel based supervised classifier to execute change detection for multi-temporal remote sensing data is a common method for flood disaster monitoring and assessment, and kernel Fisher's discrimination analysis (KFDA) is one of them. Choosing training sample by visual interpretation...
Snow is an important component of the earth surface, it has a significant role in the regional climate change, natural environment and human daily lives. Improving the techniques for global and regional snow cover mapping may benefit both environment interests and hydrological application. In the past several decades, satellite remote sensing is widely used in monitoring the snow cover because it...
Approximate query processing with relatively small random samples is an effective way to deal with many queries on large databases. However, small random samples might miss relevant records for highly selective queries due to insufficient coverage. A multidimensional index tree called the k-MDI was proposed as an effective sampling scheme for highly selective decision support queries. It has been...
Software misconfigurations are responsible for a substantial part of today's system failures, causing about one-quarter of all customer-reported issues. Identifying their root causes can be costly in terms of time and human resources. We present an approach to automatically pinpoint such defects without error reproduction. It uses static analysis to infer the correlation degree between each configuration...
Information about the accuracy of state estimation results in distribution networks is crucial for an effective and safe network management. This paper explores a new method of quantifying the uncertainty of a state estimation result by using information entropy as an index for observability. This index has the ability to represent the network observability as a single continuous number, making it...
One of the enabling technologies for smart grid development are synchrophasor measurements typically performed by devices called phasor measurement unit (PMU), that produce synchronized subsecond high-resolution voltage and current measurements, so greatly augment the traditional response time of supervisory control and data acquisition measurements. In this paper we present an algorithm that measures...
Feature location is a program comprehension activity in which a developer inspects source code to locate the classes or methods that implement a feature of interest. Many feature location techniques (FLTs) are based on text retrieval models, and in such FLTs it is typical for the models to be trained on source code snapshots. However, source code evolution leads to model obsolescence and thus to the...
In order to improve the accuracy of channel estimation in OFDM system, an improved algorithm Regularized Orthogonal Matching Pursuit (ROMP) Algorithm is proposed. The algorithm avoids choosing the wrong group and makes the group of atoms' energy more concentrated which is selected by modified regularized principle, then adds the secondary screening of estimation results, achieves an accurate reconstruction...
Software evolution has been extensively studied in the past decade for various properties and interesting patterns. In this work, we study the effect of evolution on branch prediction techniques. Typically for any program, at the hardware level, all dynamic branch prediction strategies learn the branch behaviors at run time and later re-use them to predict the direction of future branches. The duration...
Stereo-footprint is a vital trace evidence of criminal detection. Thus, it is important to make a high accuracy feature extraction of the stereo-footprint. In this paper, a system of stereo-footprint data acquisition-recognition is designed. In the system, feature extraction is completed through CP35MHT80 which offered by Wenglor, image extraction is gotten through C920 which is offered by Logitech...
With the development of computer vision technology, many researches about feature detectors and descriptors have been published in the last decades. In order to explore what kind of approaches are appropriate for unmanned aerial vehicle (UAV) onboard video processing, the popular feature detectors and descriptors are analyzed and combined with each other. Three practical videos captured in indoor...
For point-wise gamut mapping, the largest challenge is how to preserve both detail and saturation with state of the art gamut mapping algorithms based on the main concepts of gamut clipping or gamut compression. Based on a combination of clipping and compression algorithms, a hybrid point-wise gamut mapping framework is proposed and evaluated in this paper. Using five standard scenes, five combination...
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