Nonvolatile memory device using indium selenide nanowire as programmable resistive element was fabricated and its resistive switching property was studied as functions of electrical pulse width and voltage magnitude. The nanowire memory can be repeatedly switched between high-resistance (∼1011Ω) and low-resistance (∼6×105Ω) states which are attributed to amorphous and crystalline states, respectively. Once set to a specific state, the nanowire resistance is stable as measured at voltages up to 2V. This observation suggests that the nanowire can be programed into two distinct states with a large on-off resistance ratio of ∼105 with significant potential for nonvolatile information storage.

The authors report the synthesis of one-dimensional indium selenide nanowire, a III-VI group compound semiconductor nanostructure with potential applications in data storage, solar cells, and optoelectronics. Nanoscale gold particles were used as catalysts and growth was also demonstrated using indium as self-catalyst. The growth mechanism is confirmed to be vapor-liquid-solid process by in situ heating experiments in which In and Se were found to diffuse back into the gold catalyst bead forming a Au–In–Se alloy that was molten at elevated temperatures. The morphology, composition, and crystal structure of the In2Se3 nanowires (NWs) were analyzed by scanning electron microscopy, energy dispersive x-ray spectroscopy, and high-resolution transmission electron microscopy.

Authentication systems using gait captured from inertial sensors have been recently developed to enhance the limitation of existing mechanisms on mobile devices and achieved promising results. However, most these systems employed pattern recognition and machine learning techniques in which biometric templates are stored insecurely, which could leave critical security and user privacy issues. Specifically, a compromise of original gait templates could result in everlasting forfeiture. In this paper, two main results will be presented. Firstly, we propose a novel gait authentication system on mobile devices in which the security and privacy are preserved by employing a fuzzy commitment scheme. Instead of storing original gait templates for user verification like in conventional approaches, we verify the user via a stored key which is biometrically encrypted by gait templates collected from a mobile accelerometer. Secondly, the discriminability of sensor-based gait templates are investigated to determine appropriate parameter values to construct an effective gait-based biometric cryptosystem. The performance of our proposed system is evaluated on the dataset including gait signals of 34 volunteers. We achieved the zero-FAR and the False Rejection Rate of approximately 16.18 % corresponding to the key length, as well as the system security level of 139 bits. The results from our experiment show that accelerometer-based gait could be further investigated to construct a biometric cryptosystem, as effective as other biometric traits such as iris, fingerprint, voice, and signature.

Abstract. In this paper, we propose a novel gait authentication mechanism by mining sensor resources on mobile phone. Unlike previous works, both built-in accelerometer and magnetometer are used to handle mobile installation issues, including but not limited to disorientation, and misplacement errors. The authentication performance is improved by executing deep examination at pre-processing steps. A novel and effective segmentation algorithm is also provided to segment signal into separate gait cycles with perfect accuracy. Subsequently, features are then extracted on both time and frequency domains. We aim to construct a lightweight but high reliable model; hence feature subsets selection algorithms are applied to optimize the dimension of the feature vectors as well as the processing time of classification tasks. Afterward, the optimal feature vector is classified using SVM with RBF kernel. Since there is no public dataset in this field to evaluate fairly the effectiveness of our mechanism, a realistic dataset containing the influence of mobile installation errors and footgear is also constructed with the participation of 38 volunteers (28 males, 10 females). We achieved the accuracy approximately 94.93% under identification mode, the FMR, FNMR of 0%, 3.89% and processing time of less than 4 seconds under authentication mode.