2012, Vol.17, No.3

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

A low-complexity Multiplication over GF(2m) and multiplier circuit has been proposed by using cyclic-shift coefficients and the irreducible trinomial. The proposed circuit has the parallel input/output architecture and shows the lower-complexity than others with the characteristics of the cyclic-shift coefficients and the irreducible trinomial modular computation. The proposed multiplier is composed of 2-input AND gates and 2-input XOR gates without the memories and switches. And the minimum propagation delay is . The Proposed circuit architecture is well suited to VLSI implementation because it is simple, regular and modular.

We present the method to infer Context-Free Grammars by applying genetic algorithm to the Binary Third-order Recurrent Neural Networks(BTRNN). BTRNN is a multiple-layered architecture of recurrent neural networks, each of which is corresponding to an input symbol, and is combined with external stack. All parameters of BTRNN are represented as binary numbers and each state transition is performed with any stack operation simultaneously. We apply Genetic Algorithm to BTRNN chromosomes and obtain the optimal BTRNN inferring context-free grammar of positive and negative input patterns. This proposed method infers BTRNN, which includes the number of its states equal to or less than those of existing methods of Discrete Recurrent Neural Networks, with less examples and less learning trials. Also BTRNN is superior to the recent method of chromosomes representing grammars at recognition time complexity because of performing deterministic state transitions and stack operations at parsing process. If the number of non-terminals is p, the number of terminals q, the length of an input string k, and the max number of BTRNN states m, the parallel processing time is O(k) and the sequential processing time is O(km).

Balance measurement is widely used in fields of physiotherapy, neurology, sport medicine and rehabilitation medicine. This paper presents the development of balance measurement and rehabilitation training system. To this end, we have developed a prototype system using an 3-axis accelerometer sensor attached to the under surface of balance board. As a results, the system is stable and shows a good degree of balance function. Also, the various patterns for rehabilitation training can be added easily. This system can be used for daily balance monitoring and will contribute to increasing the effectiveness of training.