Images can communicate a service, brand or product. Moreover, images provide depth and context to a description or story and give a much more intense experience than writing alone. Image retrieval is the highest searc...
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Time is considered one of the main important challenges facing us today this is due to speed development technologies and increase number of activities required to achieves from any person in specific time. therefore,...
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Local feature description is gaining a lot of attention in the fields of texture classification, image recognition, and face recognition. In this paper, we propose Center-Symmetric Local Derivative Mapped patterns (CS...
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ISBN:
(纸本)9789811075124;9789811075117
Local feature description is gaining a lot of attention in the fields of texture classification, image recognition, and face recognition. In this paper, we propose Center-Symmetric Local Derivative Mapped patterns (CS-LDMP) and eXtended Center-Symmetric Local Mapped patterns (XCS-LMP) for local description of images. Strengths from Center-Symmetric Local Derivative pattern (CS-LDP) which is gaining more texture information and Center-Symmetric Local Mapped pattern (CS-LMP) which is capturing nuances between images were combined to make the CS-LDMP, and similarly, we combined CS-LMP and eXtended Center-Symmetric Local Binary pattern (XCS-LBP), which is tolerant to illumination changes and noise were combined to form XCS-LMP. the experiments were conducted on the CIFAR10 dataset and hence proved that CS-LDMP and XCS-LMP perform better than its direct competitors.
the proceedings contain 17 papers. the topics discussed include: SuperMod - a model-driven tool that combines version control and software product line engineering;semantic version management based on formal certifica...
ISBN:
(纸本)9789897581151
the proceedings contain 17 papers. the topics discussed include: SuperMod - a model-driven tool that combines version control and software product line engineering;semantic version management based on formal certification;Java-meets Eclipse - an IDE for teaching Java following the object-later approach;systematic identification of information flows from requirements to support privacy impact assessments;model checking to improve precision of design pattern instances identification in OO systems;transformation from R-UML to R-TNCES: new formal solution for verification of flexible control systems;a tool for management of knowledge dispersed throughout multiple references;a pi-calculus-based approach for the verification of UML2 sequence diagrams;on a-posteriori integration of Ecore models and hand-written Java code;OCL for rich domain models implementation - an incremental aspect based solution;and novel approach for computing skyline services with fuzzy consistent model for QoS- based service composition.
the frame sync word (SW) is very important for burst data communication systems. the misdetection (including false alarm and detection failure) probability is a very good performance metric of a SW. Usually, the exact...
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ISBN:
(纸本)9781424408009
the frame sync word (SW) is very important for burst data communication systems. the misdetection (including false alarm and detection failure) probability is a very good performance metric of a SW. Usually, the exact misdetection probability of one SW is obtained by Exhaustively Full Searching (EFS) through the whole error pattern space. EFS is straightforward, but the drawback is complexity. In this paper, we propose a new full search algorithm-Backtracking Full Searching (BFS), which is based on the backtracking method. Simulation result shows that BFS algorithm is very efficient and flexible.
this paper propose a tractable yet realistic detection model for Line-of-Sight (LoS) measurements/sensors that permits efficient multi-object tracking via the generalized labeled multi-Bernoulli (GLMB) filter. Most de...
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In group target tracking, spawning is an important event of group target motion. this paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter based on group target spawning. the algorithm models the target exte...
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Ecommerce business constantly decides innovative strategies to increase their sales and hence earn profit. they mainly strive to boost the sale of those items that are rarely purchased. there are few borderline-rare i...
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ISBN:
(纸本)9781538630778
Ecommerce business constantly decides innovative strategies to increase their sales and hence earn profit. they mainly strive to boost the sale of those items that are rarely purchased. there are few borderline-rare items that lie just below the minimum support threshold and may have a strong correlation with frequent items. the minimum support threshold is the user-defined minimum support value for an item. If these borderline-rare items are strategically placed in the market then it can help the e-commerce industry to improve their sales further. In this paper, we propose a hybrid approach, MSD-Apriori to discover borderline-rare elements which are below but close to minimum support threshold and have strong correlation with frequent items. the hybrid approach is formed by integrating MS Apriori with Dynamic Apriori. MS Apriori finds the borderline-rare item sets from the web logs and Dynamic Apriori discovers those items among these that share strong correlation withthe frequent items by association rule mining. the proposed method is evaluated on Kosarak, a real dataset that gives encouraging results.
this paper examines the human expressive states dependent on facial pictures utilizing a few viable component extraction methods. It reproduces the K-Nearest Neighbor (k-NN) classifier to approve the adequacy of succe...
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the Internet of things technology is developing rapidly, and the data generated has also exploded. Traditional cloud computing technology can no longer meet the demand for efficient processing of massive data. Edge co...
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the Internet of things technology is developing rapidly, and the data generated has also exploded. Traditional cloud computing technology can no longer meet the demand for efficient processing of massive data. Edge computing technology can move the amount of calculation down to the edge of the network, which can greatly improve computing efficiency. Applying edge computing to the field of equipment health prediction, the combination of strong responsiveness and computing capabilities of edge computing and high-precision prediction technology makes production operation and maintenance more reliable and efficient. At the same time, a neural network prediction model combining Variational Auto-Encoder (VAE) and Time Convolutional Network (TCN) is proposed to improve the accuracy of equipment health prediction. this model uses VAE for dimensionality reduction, extracts the hidden information in the original data, reconstructs high-quality sample data, and then uses TCN to mine the internal connection between the features and the target in the long sequence information. Compared with five benchmark prediction models on the C-MAPSS dataset, experiments show that the proposed model has higher prediction accuracy. (C) 2021the Authors. Published by Elsevier B.V.
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