Bogdan Belean

FPGA based system for automatic cDNA microarray image processing

Automation is an open subject in DNA microarray image processing, aiming reliable gene expression estimation. The paper presents a novel shock filter based approach for automatic microarray grid alignment. The proposed method brings up significantly reduced computational complexity compared to state of the art approaches, while similar results in terms of accuracy are achieved. Based on this approach, we also propose an FPGA based system for microarray image analysis that eliminates the shortcomings of existing software platforms: user intervention, increased computational time and cost. Our system includes application-specific architectures which involve algorithm parallelization, aiming fast and automated cDNA microarray image processing. The proposed automated image processing chain is implemented both on a general purpose processor and using the developed hardware architectures as co-processors in a FPGA based system. The comparative results included in the last section show that an important gain in terms of computational time is obtained using hardware based implementations.

Low-complexity PDE-based approach for automatic microarray image processing

Abstract Microarray image processing is known as a valuable tool for gene expression estimation, a crucial step in understanding biological processes within living organisms. Automation and reliability are open subjects in microarray image processing, where grid alignment and spot segmentation are essential processes that can influence the quality of gene expression information. The paper proposes a novel partial differential equation (PDE)-based approach for fully automatic grid alignment in case of microarray images. Our approach can handle image distortions and performs grid alignment using the vertical and horizontal luminance function profiles. These profiles are evolved using a hyperbolic shock filter PDE and then refined using the autocorrelation function. The results are compared with the ones delivered by state-of-the-art approaches for grid alignment in terms of accuracy and computational complexity. Using the same PDE formalism and curve fitting, automatic spot segmentation is achieved and visual results are presented. Considering microarray images with different spots layouts, reliable results in terms of accuracy and reduced computational complexity are achieved, compared with existing software platforms and state-of-the-art methods for microarray image processing.

Adaptive Microarray Image Acquisition System and Microarray Image Processing Using FPGA Technology

The present paper proposes an adaptive hardware implementation for a microarray image acquisition system, which is mandatory for implementing hardware algorithms for processing microarray images. Processing techniques for microarray image are also described, together with a hardware implementation of a spot border detection algorithm. The hardware implementation takes advantage of parallel computation capabilities offered by FPGA technology. Results which prove time and cost efficiency are presented for both hardware implementations.

FPGA technology and parallel computing towards automatic microarray image processing

Automation, computational time and cost are open subjects in microarray image processing. The present paper proposes image processing techniques together with their implementations in order to eliminate the shortcomings of the existing software platforms for microarray image processing: user intervention, increased computational time and cost. Thus, for each step of microarray image processing, application-specific hardware architectures are designed aiming algorithms parallelization for fast processing. Computational time is estimated and compared with state of the art approaches. The proposed hardware architectures integrated inside microarray scanners deliver microarray image characteristics in an automated manner, excluding the need of an additional software platform. The FPGA technology was chosen for implementation, due to its parallel computation capabilities and ease of reconfiguration.

Application specific hardware architecture for high-throughput short-length LDPC decoders

LDPC codes have been intensively used in various wireless communication applications, due to their increased BER performance. The present paper summarizes the state of the art applications of short length LDPC codes and proposes FPGA based application specific hardware architectures for short-length LDPC decoders. The decoding algorithms considered for implementation are both belief propagation and min-sum algorithm. Due to the increased BER performances, the proposed architecture make use of parallel computation capabilities offered by FPGA technology in order to implement the belief propagation algorithm. In spite of the iterative nature and increased computational complexity of the LDPC decoding algorithm, the proposed architecture achieves high-throughput, mandatory in real-time application and data transmission. The architecture for the LDPC belief propagation based decoder is based on arctangent hyperbolic function approximation used for check nodes update.

Two way clustering of microarray data using a hybrid approach

The Microarray technique is rather powerful, as it allows to test up thousands of genes at a time, but this produces an overwhelming set of data files containing huge amounts of data, which is quite difficult to pre-process, separate, classify and correlate for interesting conclusions to be extracted. Modern machine learning, data mining and clustering techniques based on information theory, are needed to read and interpret the information contents buried in those large data sets. Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify samples. In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.

Unsupervised image segmentation for microarray spots with irregular contours and inner holes

BackgroundMicroarray analysis represents a powerful way to test scientific hypotheses on the functionality of cells. The measurements consider the whole genome, and the large number of generated data requires sophisticated analysis. To date, no gold-standard for the analysis of microarray images has been established. Due to the lack of a standard approach there is a strong need to identify new processing algorithms.MethodsWe propose a novel approach based on hyperbolic partial differential equations (PDEs) for unsupervised spot segmentation. Prior to segmentation, morphological operations were applied for the identification of co-localized groups of spots. A grid alignment was performed to determine the borderlines between rows and columns of spots. PDEs were applied to detect the inflection points within each column and row; vertical and horizontal luminance profiles were evolved respectively. The inflection points of the profiles determined borderlines that confined a spot within adapted rectangular areas. A subsequent k-means clustering determined the pixels of each individual spot and its local background.ResultsWe evaluated the approach for a data set of microarray images taken from the Stanford Microarray Database (SMD). The data set is based on two studies on global gene expression profiles of Arabidopsis Thaliana. We computed values for spot intensity, regression ratio, and coefficient of determination. For spots with irregular contours and inner holes, we found intensity values that were significantly different from those determined by the GenePix Pro microarray analysis software. We determined the set of differentially expressed genes from our intensities and identified more activated genes than were predicted by the GenePix software.ConclusionsOur method represents a worthwhile alternative and complement to standard approaches used in industry and academy. We highlight the importance of our spot segmentation approach, which identified supplementary important genes, to better explains the molecular mechanisms that are activated in a defense responses to virus and pathogen infection.

FPGA based hardware architectures for iterative algorithms implementations

The paper describes the FPGA technology together with its possibility to exploit spatial and temporal parallelism in order to implement hardware architectures for iterative algorithms. The development of hardware architecture using FPGA technology represents a reliable solution in case of various applications where fast processing in case of iterative algorithms its mandatory. Two applications are presented where the FPGA technology is used for processing. Thus, on one hand, automatic microarray grid alignment is performed using FPGA based hardware architecture, while on the other hand, an FPGA based LDPC decoder implementation is proposed in order to improve the decoder throughput compared to state of the art approaches.

Hardware implementation of microarray image segmentation

The present paper proposes an acquisition system for microarray image on an FPGA based platform, together with a hardware implementation of image segmentation for cDNA micro-array images. The hardware implementation takes advantage of parallel computation capabilities offered by FPGA technology.

Electric generator driven by paraboloid solar concentrator

The use of the electric generators to convert the solar energy seems to be, for the next twenty years at least, a good solution, compared with photo voltaic systems, considering their higher efficiency. This paper presents the full design of a low-medium power electric solar collector with Stirling engine driven electric generator. We had to solve four main issues: the optimization of the geometry and dimensions of the parabolic solar concentrator, the thermal modeling of the heat receiver, the two rectangular coordinates sun tracking system and the choice of the appropriate Stirling engine model for the electrical generator driving. The system was designed for the specific weather conditions and the annual solar radiation characteristics of the Transylvanian plateau.