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Reed-Solomon code or RS code is widely used for error corrections of data in transmission and storages. However, it is thought of as insecure for direct implementation in code-based cryptography due to plaintext-known attacks. In recent years, McEliece cryptosystem with enhanced public key security by generalized RS code and Goppa code are discussed for hardware implementation. In this work, from...
Vehicle identification is one of the frequently studied problems in video surveillance. Commonly, identifying an unknown vehicle object requires a large amount of training instances. Unfortunately, in the large parking scenario, the cost may be prohibitively expensive because of the finitely waiting time from the car owners. In this paper, we show that it is possible to identify a registered vehicle...
BCH code for correcting distributive errors has been widely used in Nand Flashes with data-based mobile systems around the world, and its decoding process is often followed by Chien search, which calculates the roots of a polynomial in finite fields by substitution and testing. Usually, BCH decoding can be accelerated by parallel syndrome computation and Chien search. In the past years the parallel...
Hardware implementation of multiprecision modular multiplications in public-key cryptography has led to various developments of scalable Montgomery modular multipliers, which are fast enough constrained from limited hardware resources. Among the candidates, the feedforward scalable Montgomery modular multiplier achieves low latency with simple logics and little change in the algorithm. However, the...
Non-negative matrix factorization (NMF) is useful to find basis information of non-negative data. Currently, the multiplicative updates proposed by Lee and Seung are simple and popular way to find the factorization. However, this multiplicative approach of minimizing the Euclidean distance between approximate and true values is inefficient. In this paper, firstly, the original data set were partitioned...
The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming...
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