Applications of Neutrosophic Bipolar Fuzzy Sets in HOPE Foundation for Planning to Build a Children Hospital with Different Types of Similarity Measures

In this paper we provide an application of neutrosophic bipolar fuzzy sets in daily life’s problem related with HOPE foundation that is planning to build a children hospital, which is the main theme of this paper. For it we first develop the theory of neutrosophic bipolar fuzzy sets which is a generalization of bipolar fuzzy sets

HARDWARE IMPLEMENTATION OF H .264/AVC DEBLOCKING FILTER

This paper describes the design and hardware implementation of deblocking filter for reduction of blocking artifacts in latest video compression standard H.264/AVC (Advance Video Coding). H.264/AVC deblocking employ two types of filters: strongest filter and normal filter. The strongest filter utilizes maximum resources. In this paper, strongest filter of H.264/AVC is implemented on FPGA (Field Programmable Gate Array). The proposed architecture has the ability of computing the results of luminance and chrominance in fewer cycles which speeds up the filtering process. Moreover, throughput is enhanced by the use of 16 8-bit memory blocks. The proposed architecture of the filter is first implemented in MATLAB and then coded using HDL (Hardware Description Language) verilog. The proposed architecture achieves 96 clock cycles per macroblock utilizing 23K gate count, critical path delay of 9.790nsec, maximum frequency of 332MHz, and minimum frequency of 42MHz

FRACTAL COLOR IMAGE COMPRESSION USING A FAST ENCODING ALGORITHM

In this paper a fast fractal encoding algorithm based on the law of cosines is implemented in MATLAB for color images. The implementation results show that the algorithm reduces the average number of searched domain blocks for each range block by a considerable amount as compared to exhaustive search, consequentlyreducing the encoding time. In addition, we get a completely identical fractal code to that of exhaustive search resulting in the same reconstructed image quality. Moreover a general iterative fractal decoding algorithm is also implemented and the PSNRs of the decoded RGB components are plotted against the number of decoderiterations, These convergence plots show that the components reach the fixed point after 6-7 decoding iterations

Adaptive Framework for Multi-Feature Hybrid Object Tracking

Object tracking is a computer vision task deemed necessary for high-level intelligent decision-making algorithms. Researchers have merged different object tracking techniques and discovered a new class of hybrid algorithms that is based on embedding a meanshift (MS) optimization procedure into the particle filter (PF) (MSPF) to replace its inaccurate and expensive particle validation processes. The algorithm employs a combination of predetermined features, implicitly assuming that the background will not change. However, the assumption of fully specifying the background of the object may not often hold, especially in an uncontrolled environment. The first innovation of this research paper is the development of a dynamically adaptive multi-feature framework for MSPF (AMF-MSPF) in which features are ranked by a ranking module and the top features are selected on-the-fly. As a consequence, it improves local discrimination of the object from its immediate surroundings. It is also highly desirable to reduce the already complex framework of the MSPF to save resources to implement a feature ranking module. Thus, the second innovation of this research paper introduces a novel technique for the MS optimization method, which reduces its traditional complexity by an order of magnitude. The proposed AMF-MSPF framework is tested on different video datasets that exhibit challenging constraints. Experimental results have shown robustness, tracking accuracy and computational efficiency against these constraints. Comparison with existing methods has shown significant improvements in term of root mean square error (RMSE), false alarm rate (FAR), and F-SCORE

Applications of Neutrosophic Bipolar Fuzzy Sets in HOPE Foundation for Planning to Build a Children Hospital with Different Types of Similarity Measures

In this paper we provide an application of neutrosophic bipolar fuzzy sets in daily life’s problem related with HOPE foundation that is planning to build a children hospital, which is the main theme of this paper. For it we first develop the theory of neutrosophic bipolar fuzzy sets which is a generalization of bipolar fuzzy sets. After giving the definition we introduce some basic operation of neutrosophic bipolar fuzzy sets and focus on weighted aggregation operators in terms of neutrosophic bipolar fuzzy sets. We define neutrosophic bipolar fuzzy weighted averaging ( N B FWA ) and neutrosophic bipolar fuzzy ordered weighted averaging ( N B FOWA ) operators. Next we introduce different kinds of similarity measures of neutrosophic bipolar fuzzy sets. Finally as an application we give an algorithm for the multiple attribute decision making problems under the neutrosophic bipolar fuzzy environment by using the different kinds of neutrosophic bipolar fuzzy weighted/fuzzy ordered weighted aggregation operators with a numerical example related with HOPE foundation

Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor

International audienceSelf-mixing interferometry (SMI) is an attractive sensing scheme which typically relies on mono-modal operation of employed laser diode. However, change in laser modality can occur due to change in operating conditions. So, detection of occurrence of multi-modality in SMI signals is necessary to avoid erroneous metric measurements. Typically, processing of multi-modal SMI signals is a difficult task due to the diverse and complex nature of such signals. However, the proposed techniques can significantly ease this task by identifying the modal state of SMI signals with 100% success rate, so that interferometric fringes can be correctly interpreted for metric sensing applications