Graphene is a promising material for its exceptional electrical and mechanical properties. Starting with the initial demonstration of isolating a single graphene sheet from graphite, much progress has been made in realizing graphene based devices for diverse applications. Here, we introduce an experiment in which the electrical properties of graphene are modified by coating different-sequence single-stranded deoxyribonucleic acid (ssDNA) molecules. We fabricated a graphene-field effect transistor (FET) by transferring CVD graphene on copper foil onto a Si/SiO2 wafer. A passivation layer opened up windows on the surface of the graphene to enable interaction with liquid buffers. ssDNA molecules with different base sequences were coated onto the active graphene channels. We observed a variation in the Dirac voltage of the ssDNA-coated graphene FETs according to the ssDNA base sequences. Electrical control of the graphene FET is obtained via gating effect of the deposited ssDNAs. We conduct a systematic study of this ssDNAinduced gating effect with different base sequences, concentrations, and lengths of molecules, leading to extraction of characteristic parameters of the graphene FET accordingly.
Graphene is a promising material for vapor sensor applications because of its potential to be functionalized for specific chemical gases. In this work, we present a graphene gas sensor that uses single-stranded DNA (ssDNA) molecules as its sensing agent. We investigate the characteristics of graphene field effect transistors (FETs) coated with different ssDNAs. The sensitivity and recovery rate for a specific gas are modified according to the differences in the DNA molecules’ Guanine (G) and Cytosine (C) content. ssDNA-functionalized devices show a higher recovery rate compared to bare graphene devices. Pattern analysis of a 2-by-2 sensor array composed of graphene devices functionalized with different-sequence ssDNA enables identification of NH<sub>3</sub>, NO<sub>2</sub>, CO, SO<sub>2</sub> using Principle Component Analysis (PCA).
Recently, gaze detection-based interfaces have been regarded as the most natural user interface for use with smart televisions (TVs). Past research conducted on gaze detection primarily used near-infrared (NIR) cameras with NIR illuminators. However, these devices are difficult to use with smart TVs; therefore, there is an increasing need for gaze-detection technology that utilizes conventional (visible light) web cameras. Consequently, we propose a new gaze-detection method using a conventional (visible light) web camera. The proposed approach is innovative in the following three ways. First, using user-dependent facial information obtained in an initial calibration stage, an accurate head pose is calculated. Second, using theoretical and generalized models of changes in facial feature positions, horizontal and vertical head poses are calculated. Third, accurate gaze positions on a smart TV can be obtained based on the user-dependent calibration information and the calculated head poses by using a low-cost conventional web camera without an additional device for measuring the distance from the camera to the user. Experimental results indicate that the gaze-detection accuracy of our method on a 60-in. smart TV is 90.5%.
Biometrics is a method of identifying individuals by their physiological or behavioral characteristics. Among other biometric identifiers, iris recognition has been widely used for various applications that require a high level of security. When a conventional iris recognition camera is used, the size and position of the iris region in a captured image vary according to the X , Y positions of a user’s eye and the Z distance between a user and the camera. Therefore, the searching area of the iris detection algorithm is increased, which can inevitably decrease both the detection speed and accuracy. To solve these problems, we propose a new method of iris localization that uses wide field of view (WFOV) and narrow field of view (NFOV) cameras. Our study is new as compared to previous studies in the following four ways. First, the device used in our research acquires three images, one each of the face and both irises, using one WFOV and two NFOV cameras simultaneously. The relation between the WFOV and NFOV cameras is determined by simple geometric transformation without complex calibration. Second, the Z distance (between a user’s eye and the iris camera) is estimated based on the iris size in the WFOV image and anthropometric data of the size of the human iris. Third, the accuracy of the geometric transformation between the WFOV and NFOV cameras is enhanced by using multiple matrices of the transformation according to the Z distance. Fourth, the searching region for iris localization in the NFOV image is significantly reduced based on the detected iris region in the WFOV image and the matrix of geometric transformation corresponding to the estimated Z distance. Experimental results showed that the performance of the proposed iris localization method is better than that of conventional methods in terms of accuracy and processing time.
For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris’s image is frequently rotated because of the user’s head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the averaged equal error rate of iris recognition using the proposed method was 4.3006%, which is lower than that of other methods.