Content-Based Image Retrieval (CBIR) allows automatically extracting target images according to objective visual contents of the image itself. Content-based image retrieval has many application areas such as, education, commerce, military, searching, biomedicine and web image classification. This paper proposes a new image retrieval system, which uses color and texture information to form the feature vectors and Bhattacharyya distance and new similarity measure to perform the feature matching. This framework integrates the yc b c r color histogram which represents the global feature and edge histogram as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard image databases such as Wang and UCID databases. Experimental work shows that the proposed approach improves the precision and recall of retrieval results compared to other approaches reported in literature.
Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoidance of human errors due to inconsistency and fatigue. In this paper, the Support Vector Machine (SVM) classifier with various kernels is investigated for the categorization of machined surfaces into the six machining processes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. The effectiveness of the gray-level histogram as the discriminating feature is explored. Experimental results suggest that the SVM with the linear kernel provides superior performance for a dataset consisting of 72 workpiece images.
Wireless sensor networks (WSNs) are set of sensor nodes to monitor and detect transmitted data to the sink. WSNs face significant challenges in terms of node energy availability, which may impact network sustainability. As a result, developing protocols and algorithms that make the best use of limited resources, particularly energy resources, is critical issues for designing WSNs. Routing algorithms, for example, are unique algorithms as they have a direct and effective relationship with lifetime of network and energy. The available routing protocols employ single-hop data transmission to the sink and clustering per round. In this paper, a Fuzzy Clustering and Energy Efficient Routing Protocol (FCERP) that lower the WSNs energy consuming and increase the lifetime of network is proposed. FCERP introduces a new cluster-based fuzzy routing protocol capable of utilizing clustering and multiple hop routing features concurrently using a threshold limit. A novel aspect of this research is that it avoids clustering per round while considering using fixed threshold and adapts multi-hop routing by predicting the best intermediary node for clustering and the sink. Some Fuzzy factors such as residual energy, neighbors amount, and distance to sink considered when deciding which intermediary node to use.
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