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Single electron devices have extremely poor driving capabilities so that direct application to practical circuits is as yet almost impossible. A new methodology to overcome this problem is to build hybrid circuits consisting of single electron transistors (SETs) and CMOS interfaces. In this work a room temperature operable hybrid CMOS–SET inverter circuit, hybrid CMOS–SET NOR gate and their Voltage...
There was an emotional outpouring of unprecedented magnitude because of the death of Michael Jackson thereby creating fresh interest to test various methods of sentiment analysis to obtain insights into how a human beings show emotion in different limitations. Almost no research work has been done, as on date, based on nouns and adverb-adjective-noun (AAN) combinations in sentiment analysis. We have...
Hybrid SET-CMOS circuits which combine the merits of both the SET and CMOS promises to be a practical implementation for future low power ultra-dense VLSI/ULSI circuit design. In this work, an SET-CMOS hybrid pulse divider circuit is proposed. The MIB model for SET and BSIM4 model for CMOS are used. The operation of the proposed circuit is verified in Tanner environment. The performances of CMOS and...
This paper proposes a multimodal biometric system through Gaussian mixture model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear...
Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT)...
Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of...
Multi-biometric systems have many advantages over the uni-biometric systems. However, multi-biometric systems lacking in many respects, such as multimodal systems not only acquire relevant and viable information for fusion, but also acquire some irrelevant and redundant information which are associated to the feature sets or with the match score sets, and this may lead to the resultant performance...
This paper presents a novel biometric sensor generated evidence fusion of face and palmprint images using wavelet decomposition for personnel identity verification. The approach of biometric image fusion at sensor level refers to a process that fuses multispectral images captured at different resolutions and by different biometric sensors to acquire richer and complementary information to produce...
The conventional kernel PCA does not really nonlinearly maps an input image into a high-dimensional feature space. Rather, it chooses a kernel function a priori and computes the principal components indirectly within the input space spanned by the image pixels. Thus method does not consider the structural information of the input images in the feature space. Therefore, the computed principal components...
In this paper, an efficient method for face recognition using principal component analysis (PCA) and radial basis function (RBF) neural networks is presented. Recently, the PCA has been extensively employed for face recognition algorithms. It is one of the most popular representation methods for a face image. It not only reduces the dimensionality of the image, but also retains some of the variations...
This paper presents an approach to face recognition based on Dempster-Shafer (DS) theory of evidence, which combines the evidences of two radial basis function (RBF) neural networks. The degrees of belief of the two RBF neural networks for classification of an image have been estimated using two different feature vectors derived from images of the ORL face database. Then these degrees of belief have...
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