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Boron carbide (B4C) is an essential material in various high-performance applications due to its light weight and hardness. In this work, B4C-based composites were fabricated via a powder route consisting of powder mixing, precursor preparation, and hot-pressing under vacuum. The composites' mechanical properties and microstructure were analyzed to investigate the effect of adding minor second-phase particles. In addition to homogenizing the grain size, the addition of SiC (≤10 wt%) to B4C increased its strength and improved its fracture toughness, with values reaching 551 MPa and 3.22 MPa m1/2, respectively. Meanwhile, the addition of TiB2 (≤10 wt%) significantly improved the strength and fracture toughness only, with values reaching 548 MPa and 3.92 MPa m1/2, respectively, with only a minimal decrease in hardness. Microstructural analysis revealed that the second-phase particles were uniformly distributed and reduced the average grain size, contributing to the increase in strength. Additionally, the TiB2 particles impeded crack propagation and induced crack deflection at the interface, indicating the formation of an intergranular fracture mode. On the contrary, the addition of SiC primarily resulted in transgranular fracture behavior, though it still improved the toughness of the B4C. These results suggest that small amounts of SiC and TiB2 can effectively enhance the mechanical properties of B4C ceramics while maintaining the lightweight characteristics critical for military and aerospace applications. [ABSTRACT FROM AUTHOR]

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods.

Researches on Augmented Reality (AR) have recently received attention. With these, the Machine-to-Machine (M2M) market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M market has applied the existing marker-based AR technology in entertainment, business and other industries. With the existing marker-based AR technology, a designated object can only be loaded on the screen from one marker and a marker has to be added to load on the screen the same object again. This situation creates a problem where the relevant marker'should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. In this paper, software-based sensing technology for multiple object detection and loading using PPHT has been developed and overlapping marker control according to multiple object control has been studied using the Bresenham and Mean Shift algorithms.

This research suggests a Denial of Service (DoS) detection method based on the collection of interdependent behavior data in a sensor network environment. In order to collect the interdependent behavior data, we use a base station to analyze traffic and behaviors among nodes and introduce methods of detecting changes in the environment with precursor symptoms. The study presents a DoS Detection System based on Global Interdependent Behaviors and shows the result of detecting a sensor carrying out DoS attacks through the test-bed.