Marcos Santana Farias

A hardware architecture for subtractive clustering

Clustering algorithms are used extensively to organise and categorise abundant data. This paper describes the implementation of subtractive clustering algorithm in hardware. The solution developed in this paper seeks a hardware implementation to automatic and fast identification of cluster centres. This hardware proposed is generic so it can be used in any data classification problems, omnipresent in identification systems.

Efficient hardware architecture for embedded radionuclide identification

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 a hardware architecture to perform radionuclide identification. We also show a comparison between the performance of the proposed architecture and other solutions.

Hardware implementation of subtractive clustering for radionuclide identification

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.

Reconfigurable hardware to radionuclide identification using subtractive clustering

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.

A massively parallel hardware for modular exponentiations using the m -ary method

Most of cryptographic systems are based on modular exponentiation. It is performed using successive modular multiplications. One way of improving the throughput of a cryptographic system implementation is reducing the number of the required modular multiplications. Existing methods attempt to reduce this number by partitioning the exponent in constant or variable size windows. In this paper, in the purpose of further accelerating the computation of modular exponentiation, a concurrent novel approach is proposed along with hardware implementation of the concurrent m-ary method. We compare the proposed method to the sequential implementation.