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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Reconfiguration-based implementation of SVM classifier on FPGA for Classifying Microarray data.

Hanaa M Hussain, Khaled Benkrid, Huseyin Seker

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a Field Programmable Gate Array (FPGA) implementation for Support Vector Machines (SVM) classifiers, accelerating Microarray data analysis. The hardware-based SVM achieved an 85x speed-up, demonstrating FPGA efficiency for high-dimensional data classification.

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    Area of Science:

    • Bioinformatics
    • Computer Engineering
    • Computational Biology

    Background:

    • High-dimensional Microarray data classification demands significant computational power.
    • Support Vector Machines (SVM) are effective but computationally intensive classifiers for Microarray analysis.
    • Hardware acceleration can address the computational challenges of SVM algorithms.

    Purpose of the Study:

    • To present a flexible, dynamically and partially reconfigurable Field Programmable Gate Array (FPGA) implementation of an SVM classifier.
    • To accelerate the classification of high-dimensional Microarray data using hardware-based SVM.
    • To evaluate the performance enhancement of SVM on FPGAs for bioinformatics applications.

    Main Methods:

    • Developed a reconfigurable SVM architecture on an FPGA.
    • Implemented SVM algorithm kernels to exploit hardware parallelism.
    • Compared FPGA performance against general-purpose processors (GPPs).

    Main Results:

    • Achieved up to an 85x speed-up in Microarray data classification compared to GPPs.
    • Demonstrated the feasibility of hardware acceleration for complex SVM algorithms.
    • Showcased the effectiveness of FPGAs for high-dimensional data analysis.

    Conclusions:

    • FPGA-based SVM implementation significantly enhances computational performance for Microarray data analysis.
    • FPGAs offer a viable solution for accelerating computationally demanding bioinformatics tasks.
    • This approach paves the way for efficient hardware acceleration in future bioinformatics applications.