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In recent years, wireless sensor networks (WSNs) have been considered as a potential solution for real-time monitoring applications and these WSNs have potential practical impact on next generation technology too. However, WSNs could become a threat if suitable security is not considered before the deployment and if there are any loopholes in their security, which might open the door for an attacker and hence, endanger the application. User authentication is one of the most important security services to protect WSN data access from unauthorized users; it should provide both mutual authentication and session key establishment services. This paper proposes a robust user authentication framework for wireless sensor networks, based on a two-factor (password and smart card) concept. This scheme facilitates many services to the users such as user anonymity, mutual authentication, secure session key establishment and it allows users to choose/update their password regularly, whenever needed. Furthermore, we have provided the formal verification using Rubin logic and compare RUASN with many existing schemes. As a result, we found that the proposed scheme possesses many advantages against popular attacks, and achieves better efficiency at low computation cost. [ABSTRACT FROM AUTHOR]

We propose a simple but effective convolutional neural network to learn the similarities between closely related raw pixel images for feature representation extraction and classification through the initialization of convolutional kernels from learned filter kernels of the network. The binary-class classification of sigmoid and discriminative feature vectors are simultaneously learned together contrasting the handcrafted traditional method of feature extractions, which split feature-extraction and classification tasks into two different processes during training. Relying on the high-quality feature representation learned by the network, the classification tasks can be efficiently conducted. We evaluated the classification performance of our proposed method using a collection of tile surface images consisting of cracked surfaces and no-cracked surfaces. We tried to classify the tiny-cracked surfaces from non-crack normal tile demarcations, which could be useful for automated visual inspections that are labor intensive, risky in high altitudes, and time consuming with manual inspection methods. We performed a series of comparisons on the results obtained by varying the optimization, activation functions, and deployment of different data augmentation methods in our network architecture. By doing this, the effectiveness of the presented model for smooth surface defect classification was explored and determined. Through extensive experimentation, we obtained a promising validation accuracy and minimal loss. [ABSTRACT FROM AUTHOR]

Three species of egg parasites, viz., Anagrus sp., A. optabilis (Mymaridae) and Oligosita sp. (Trichogrammatidae), and a nymphal/adult parasite Gonatopus sp. of rice planthoppers were studied for their biology and control potential. Larger number of adult mymarids emerged from host eggs between 8·30 a.m. and 12·30 p.m. of the day whereas trichogrammatid adults emerged between 12·30 p.m. to 4·30 p.m. All the three species parasitised both brown planthopper (BPH) and white backed planthopper (WBPH) but, in general, failed to parasitise rice leaf-hoppers. Developmental duration from oviposit ion to adult emergence noted for these parasites indicated that males of mymarids, in general, developed faster (10-11 days) than females (12-13 days) at 20-32° C prevailing during October, whereas Oligosita females developed more slowly (14-15 days). However, both A. optabilis and Oligosita developed three days faster at 30-38° C prevailing during April, Fecundity in terms of number of eggs parasitised per female varied from 12·3 to 20·3. Under greenhouse conditions, release of 1 and 5 pairs of mymarid parasites for 10 days reduced the nymphal hatch of BPH by 60 and 85%, respectively. Nymphal/adult parasite Gonatopus sp. completed its life cycle in 19·5 to 31 days on both BPH and WBPH. While the 4th and 5th instar nymphs of BPH were parasitised more frequently, green leafhopper nymphs were not parasitised. Besides being endoparasitic, the adult females also predated on and killed as many as 5·2 nymphs a day. [ABSTRACT FROM AUTHOR]