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The price of the wood according to the type of wood. Classification of the woods can be done by studying its texture. This paper introduces Fuzzy k Nearest Neighbor to classify 25 types of wood. The woods images have been taken from the Wood Database of the Centre for Artificial Intelligence & Robotics, Universiti Teknologi Malaysia. The features of wood images are extracted using Local Binary Pattern. The results of this paper shows improvement in wood classification compare to the previous literature.

Gianluca orcid.org/0000-0001-8110-8950 and Trefzer, Martin Albrecht orcid.org/0000-0002-6196-6832 (2018) Artificial bee colony-inspired run-time task management for many-core systems.Abstract-Efficient resource and application management is one of the most complex and challenging tasks in high performance computing. Large-scale computing systems that contain hundreds, thousands or even millions of cores demand solutions that can operate in a distributed, robust, and scalable fashion. However, while hardware parallelism is relatively straight forward to achieve, this is not generally the case for software. This leads to under-utilization of the hardware parallelism as well as imbalanced load distribution causing inefficiency and hotspots. In response to this challenge, this paper introduces a novel distributed and decentralized run-time management algorithm. The proposed method is guided by an optimization model inspired by artificial bee colonies (ABC). While ABC have proven useful for optimizing large sets of numerical test functions, this is the first time they are applied in the context of many-core system management. The initial result shows that, the ABC model is promising in context of run-time management for many-core systems. It is also anticipated that the algorithms bio-inspired foundations will inherently enable scalability, reliability, and adaptation. We are showing initial experiments, where the initial results indicate the capability of our model to improve the thermal distribution across the system.

The paper shows the capabilities of RRAM to model Neural Networks. The objectives of the work are to provide a likely RRAM based neuron model, and to emulate RRAM Bridge; which is used for synaptic weight adjustment. In order to analyze and design RRAM circuits, a Laplace domain expression has been derived. These equations can then be used for modeling and simulating simple neural networks.