Pattern formation is a very interesting phenomenon formed above a water anode in atmospheric pressure glow discharge. Up to now, concentric-ring patterns only less than four rings have been observed in experiments. In this paper, atmospheric pressure glow discharge above a water anode is conducted to produce diversified concentric-ring patterns. Results indicate that as time elapses, the number of concentric rings increases continuously and up to five rings have been found in the concentric-ring patterns. Moreover, the ring number increases continuously with increasing discharge current. The electrical conductivity of the anode plays an important role in the transition of the concentric patterns due to its positive relation with ionic strength. Hence, the electrical conductivity of the water anode is investigated as a function of time and discharge current. From optical emission spectrum, gas temperature and intensity ratio related with density and temperature of electron have been calculated. The various concentric-ring patterns mentioned above have been simulated at last with an autocatalytic reaction model.

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Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).

In many machine learning settings, labeled samples are difficult to collect while unlabeled samples are abundant. We investigate in this paper the design of support vector machine classification algorithms learning from positive and unlabeled samples only. We first find the minimum bounding sphere that enclosed all the positive samples, and then use this minimum bounding sphere to pick out the negative samples from the unlabeled samples, at last we train the support vector machine using the training set which consists of the given positive samples and the negative samples picked out from the unlabeled samples. Experiments indicate that support vector machine learning from positive and unlabeled samples achieves the desired high test precision and prediction accuracy.