Context Based Visual Content Verification

In this paper the intermediary visual content verification method based on multi-level co-occurrences is studied. The co-occurrence statistics are in general used to determine relational properties between objects based on information collected from data. As such these measures are heavily subject to relative number of occurrences and give only limited amount of accuracy when predicting objects in real world. In order to improve the accuracy of this method in the verification task, we include the context information such as location, type of environment etc. In order to train our model we provide new annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional properties of the image. We show that the usage of context greatly improve the accuracy of verification with up to 16% improvement.Comment: 6 pages, 6 Figures, Published in Proceedings of the Information and Digital Technology Conference, 201

Minimization of Quantum Circuits using Quantum Operator Forms

In this paper we present a method for minimizing reversible quantum circuits using the Quantum Operator Form (QOF); a new representation of quantum circuit and of quantum-realized reversible circuits based on the CNOT, CV and CV$^\dagger$ quantum gates. The proposed form is a quantum extension to the well known Reed-Muller but unlike the Reed-Muller form, the QOF allows the usage of different quantum gates. Therefore QOF permits minimization of quantum circuits by using properties of different gates than only the multi-control Toffoli gates. We introduce a set of minimization rules and a pseudo-algorithm that can be used to design circuits with the CNOT, CV and CV$^\dagger$ quantum gates. We show how the QOF can be used to minimize reversible quantum circuits and how the rules allow to obtain exact realizations using the above mentioned quantum gates.Comment: 11 pages, 14 figures, Proceedings of the ULSI Workshop 2012 (@ISMVL 2012

A Multi-Context FPGA Using a Floating-Gate-MOS Functional Pass-Gate and Its CAD Environment

科研費報告書収録論文(課題番号:17300009/研究代表者:亀山充隆/システムインテグレーション理論に基づく高安全知能自動車用VLSIの最適設計

A study of the different uses of colour channels for traffic sign recognition on hierarchical temporal memory

When designing intelligence for a car many different tasks can be performed. Some of these tasks cannot easily be performed by conventional algorithms in comparison with the human brain. Recently, such intelligence has often been reached by using probability based systems. In this paper, Hierarchical Temporal Memory (HTM) is used to implement one of these tasks, namely traffic sign recognition. In implementing this traffic sign recognition task, it is noticed that the use of colour is of particular importance, and that colour information should be treated in a particular way to optimise the recognition. However it is also noticed that there are still a significant number of differences between the modelling of the brain and how the brain actually deals with colour and object recognition