IRIS Feature Extraction and Classification using FPGA

An approach of singular value (SVD) of a (mxn) 2-D matrix has beenpopularly used by researchers for representing a 2-D image by a set of less than or equal to n values sequenced in descending order of which a subset of only first few values which are significant is treated as a set of features for that image. These features are further used for image recognition and classification. Though many papers as reviewed from literature have discussed about this implantation using software/MATLAB approach, rarely a paper appears on hardware implementation of SVD algorithm for image processing applications. This paper presents the details of a hardware architecture developed by us to implement SVD algorithm and then presents the results of implementation of this architecture in the Xilinx field programmable gate array Virtex5 to extract the features of an iris image. A comparison between the feature values extracted by MATLAB and those obtained by hardware simulation using Xilinx ISE tool indicates a very good match validating the hardware architecture. A hamming distance classifier using appropriate threshold values stored in ROM is used to classify the iris images.DOI:http://dx.doi.org/10.11591/ijece.v2i2.15

Iris: an Extensible Application for Building and Analyzing Spectral Energy Distributions

Iris is an extensible application that provides astronomers with a user-friendly interface capable of ingesting broad-band data from many different sources in order to build, explore, and model spectral energy distributions (SEDs). Iris takes advantage of the standards defined by the International Virtual Observatory Alliance, but hides the technicalities of such standards by implementing different layers of abstraction on top of them. Such intermediate layers provide hooks that users and developers can exploit in order to extend the capabilities provided by Iris. For instance, custom Python models can be combined in arbitrary ways with the Iris built-in models or with other custom functions. As such, Iris offers a platform for the development and integration of SED data, services, and applications, either from the user's system or from the web. In this paper we describe the built-in features provided by Iris for building and analyzing SEDs. We also explore in some detail the Iris framework and software development kit, showing how astronomers and software developers can plug their code into an integrated SED analysis environment.Comment: 18 pages, 8 figures, accepted for publication in Astronomy & Computin

Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing

The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices. In this work, we propose the design of an analog deep neural network with binary weight update through backpropagation algorithm using binary state memristive devices. We show that such networks can be successfully used for image processing task and has the advantage of lower power consumption and small on-chip area in comparison with digital counterparts. The proposed network was benchmarked for MNIST handwritten digits recognition achieving an accuracy of approximately 90%