Influence of Carbon Equivalent Content on Phase Transformation During Inter-critical Heating of Dual Phase Steels Using Discrete Micro-scale Cellular Automata Model

In this paper, a cellular automata method based model is proposed for simulating phase transformation kinetics of inter-critical heating of dual phase (DP) steel. This developed model deals with the kinetics of pearlite dissolution, ferrite transformation and austenite grain growth based on carbon diffusion process. Diffusion equation is discretized and solved by finite difference method whereas austenite grain growth is controlled by transition rules applied in cellular automata algorithm. The model is operated in the temperature range of 730–890 °C for four different specimens of DP steel. This model predicts appropriately the microstructure and volume fraction of formed austenite during inter-critical heating of DP steel. In addition, this study shows that the presence of carbon and alloying elements enhances carbon equivalent of DP steel, helps in austenite formation.     

An experimental study on the ballistic impact behavior of some metallic armour materials against 7.62 mm deformable projectile

The investigation describes and analyses the ballistic impact behavior of a high strength armour steel and Al-7017 alloy under 7.62 mm deformable projectiles at a velocity of 830 ± 10 m/s at normal angle of attack. The high strength armour steel is subjected to two different heat treatments to see the effect of different mechanical properties on the ballistic behavior. The ballistic result of the Al-7017 alloy is compared with that of the steel. Some observations relating to the adiabatic shear bands formation have also been presented. Experimental results showed that among the investigated materials, the best ballistic performance was attained with the armour steel at 910 °C austenitisation followed by 200 °C tempering condition.     

RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data

S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is crucial for understanding their functional consequences in physiology and pathology. We present a random forest (RF) classifier-based consensus strategy (RFCM-PALM) for predicting the palmitoylated cysteine sites on synaptic proteins from male/female mouse data. To design the prediction model, we have introduced a heuristic strategy for selection of the optimum set of physicochemical features from the AAIndex dataset using (a) K-Best (KB) features, (b) genetic algorithm (GA), and (c) a union (UN) of KB and GA based features. Furthermore, decisions from best-trained models of the KB, GA, and UN-based classifiers are combined by designing a three-star quality consensus strategy to further refine and enhance the scores of the individual models. The experiment is carried out on three categorized synaptic protein datasets of a male mouse, female mouse, and combined (male + female), whereas in each group, weighted data is used as training, and knock-out is used as the hold-out set for performance evaluation and comparison. RFCM-PALM shows ~80% area under curve (AUC) score in all three categories of datasets and achieve 10% average accuracy (male—15%, female—15%, and combined—7%) improvements on the hold-out set compared to the state-of-the-art approaches. To summarize, our method with efficient feature selection and novel consensus strategy shows significant performance gains in the prediction of S-palmitoylation sites in mouse datasets.     

Friend: a communication aid for persons with disabilities.

Computers offer valuable opportunities to people with physical disabilities. For example, a computer can allow someone with severe speech and motor-impairment to engage more fully with the world. This paper describes the design of a communication aid for motor-impaired users, who literally use computers as their communication partners. Currently, a low cost interface suitable for different types of motor-impaired users is hardly available. Additionally, the target audience of existing such systems is very much limited. The present work solved these problems by its adaptation mechanism. The adaptation mechanism provides an appropriate interface from an interface bank for each user before start of an interaction. The adaptation mechanism is continued during as well as after the end of interactions to make the system personalized to individual user.     

Neural network trained morphological processing for the detection of defects in woven fabric

Basic morphological operations such as the erosion, dilation, opening, and closing often fail to detect various types of defects that may be present in woven fabric, mainly because of the heuristic selection of structuring element needed for these operations. In this paper, an artificial neural network (ANN) is utilized for the selection of structuring element, where ANN is trained by two pre‐assigned normalized numbers related to the warp and weft counts of the test fabric. The test gray fabric image is pre‐processed to remove noise and the interlaced grating structure of weft and warp and then converted to a binary image by thresholding. An intensity threshold value of the processed fabric image and the dimension of a sliding window needed for correlation operation are obtained from the trained ANN. Defects are detected after morphological reconstruction of the processed binary fabric image, where an ANN trained structuring element is used. The technique is tested on 317 samples for eight different types of defects in three types of plain woven fabrics from TILDA database and 92.8% success of detection is achieved.     

Moving object detection and tracking in videos through turbulent medium

This paper addresses the problem of identifying and tracking moving objects in a video sequence having a time-varying background. This is a fundamental task in many computer vision applications, though a very challenging one because of turbulence that causes blurring and spatiotemporal movements of the background images. Our proposed approach involves two major steps. First, a moving object detection algorithm that deals with the detection of real motions by separating the turbulence-induced motions using a two-level thresholding technique is used. In the second step, a feature-based generalized regression neural network is applied to track the detected objects throughout the frames in the video sequence. The proposed approach uses the centroid and area features of the moving objects and creates the reference regions instantly by selecting the objects within a circle. Simulation experiments are carried out on several turbulence-degraded video sequences and comparisons with an earlier method confirms that the proposed approach provides a more effective tracking of the targets.     

A dynamic TDMA based scheme for securing query processing in WSN

Nodes in a wireless sensor network (WSN) are generally deployed in unattended environments making the nodes susceptible to attacks. Therefore, the need of defending such attacks becomes a big challenge. We propose a scheme to build a security mechanism in a query-processing paradigm within WSN. The scheme is capable of protecting replay attack while preserving essential properties of security such as authentication, data integrity and data freshness. The solution is made lightweight using symmetric key cryptography with very short-length key. Further, the key used in our scheme is neither pre-deployed nor is transmitted directly. The key information is established among nodes through an efficient use of one variant of dynamic TDMA mechanism which ensures security of key. Another variant of dynamic TDMA is used to make the scheme bandwidth saving, an essential quality of WSN. Performance of the scheme is analyzed in terms of storage, computation and communication overhead. Finally the analytical results are compared with two of the existing schemes including the previous version of the present scheme that show significant reduction of all such overheads thereby proving the suitability of the proposed scheme for a resource-constrained network like WSN.     

Synthesis of boron nitride nanotubes employing mechanothermal process and its characterization

Multi-walled boron nitride (BN) nanotubes having cylindrical structure were synthesized employing the mechanothermal process. In this process hexagonal boron nitride powder (hBN) was first ball milled for 50–100 h using a high-energy ball mill and the ball-milled samples were annealed in N₂ atmosphere for about 10 h in the temperature range of 950–1300 °C. The BN nanotubes exhibited a well-crystallized hexagonal structure with about 25–40 nm in diameter and up to 1 μm length. These BN nanotubes were well characterized using various techniques, such as, XRD, SEM, TEM and Raman Spectroscopy.     

Rough set based generalized fuzzy c-means algorithm and quantitative indices.

A generalized hybrid unsupervised learning algorithm, which is termed as rough-fuzzy possibilistic c-means (RFPCM), is proposed in this paper. It comprises a judicious integration of the principles of rough and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. It incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy c-means and the coincident clusters of PCM. The concept of crisp lower bound and fuzzy boundary of a class, which is introduced in the RFPCM, enables efficient selection of cluster prototypes. The algorithm is generalized in the sense that all existing variants of c-means algorithms can be derived from the proposed algorithm as a special case. Several quantitative indices are introduced based on rough sets for the evaluation of performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated both qualitatively and quantitatively on a set of real-life data sets.