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Modular exponentiation is a basic operation in various applications, such as cryptography. Generally, the performance of this operation has a tremendous impact on the efficiency of the whole application. Therefore, many researchers have devoted special interest to providing smart methods and efficient implementations for modular exponentiation. One of these methods is the sliding-window method, which pre- processes the exponent into zero and non-zero partitions. Zero partitions allow for a reduction of the number of modular multiplications required in the exponentiation process. In this paper, we devise a novel hardware for computing modular exponentiation using the sliding-window method. The partitioning strategy used allows variable-length non-zero partitions, which increases the average number of zero partitions and so decreases that of non-zero partitions. It performs the partitioning process in parallel with the pre-computation step of the exponent so no overhead is introduced. The implementation is efficient when compared against related existing hardware implementations. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a massively parallel architecture for hardware implementation of genetic algorithms. This design is quite innovative as it provides a viable solution to the fitness computation problem, which depends heavily on the problem-specific knowledge. The proposed architecture is completely independent of such specifics. It implements the fitness computation using a neural network. The hardware implementation of the used neural network is stochastic and thus minimise the required hardware area without much increase in response time. Last but not least, we demonstrate the characteristics of the proposed hardware and compare it to existing ones. [Copyright &y& Elsevier]

Abstract: DNA sequence matching is used in the identification of a relationship between a fragment of DNA and its owner by mean of a database of DNA registers. A DNA fragment could be a hair sample left at a crime scene by a suspect or provided by a person for a paternity exam. The process of aligning and matching DNA sequences is a computationally demanding process. In this paper, we propose a novel parallel hardware architecture for DNA matching based on the steps of the BLAST algorithm. The design is scalable so that its structure can be adjusted depending on the size of the subject and query DNA sequences. Moreover, the number of units used to perform in parallel can also be scaled depending some characteristics of the algorithm. The design was synthesized and programmed into FPGA. The trade-off between cost and performance were analyzed to evaluate different design configuration. [Copyright &y& Elsevier]

Abstract: Radioactivity is the spontaneous emission of energy from unstable atoms. Radioactive sources have radionuclides. Radionuclide undergoes radioactive decay and emits gamma rays and subatomic particles, constituting the ionizing radiation. The gamma ray energy of a radionuclide is used to determine the identity of gamma emitters present in the source. This paper describes the hardware implementation of subtractive clustering algorithm to perform radionuclide identification. The hardware was proven to be effective for this task, allowing scalability to increase parallelism in the computations. [Copyright &y& Elsevier]