10,712 research outputs found
Efficient Hardware Implementation Of Haar Wavelet Transform With Line-Based And Dual-Scan Image Memory Accesses
Image compression is of great importance in multimedia systems and applications because it drastically reduces bandwidth requirements for transmission and
memory requirements for storage. An image compression algorithm JPEG2000 isbased on Discrete Wavelet Transform. In the hardware implementation of DiscreteWavelet
Transform (DWT) and inverse DiscreteWavelet Transform (IDWT),the main problems are storage memory, internal processing buffer, and the limitation of the FPGA resources. Based on non-separable 2-D DWT, the method
used to access the image memory has a direct impact on the internal buffer size,the power consumption and, the transformation speed. The need for internal buffer reduces the image memory access time. The main objectives of this thesis are as follows; to implement a 2-D Haar wavelet transform for large gray-scale image, to reduce the number of image memory access by implementing the 2-
D Haar wavelet transform with a suitable combination between using external memory and internal memory, and targeting a low-power and high-speed architecture
based on multi-levels non-separable discrete Haar wavelet transform. In this work, the proposed two architectures reduce the number of image memory access. The line-based architecture reduces the internal buffer by 2 x 0.5 x N
where N presents the image size. This happens for the low-pass coefficients and for the high-pass coefficients. The dual-scan architecture does not use the internal
memory. Overall both architectures work well on the Altera FPGA board at frequency 100 MHz
Pruned Continuous Haar Transform of 2D Polygonal Patterns with Application to VLSI Layouts
We introduce an algorithm for the efficient computation of the continuous
Haar transform of 2D patterns that can be described by polygons. These patterns
are ubiquitous in VLSI processes where they are used to describe design and
mask layouts. There, speed is of paramount importance due to the magnitude of
the problems to be solved and hence very fast algorithms are needed. We show
that by techniques borrowed from computational geometry we are not only able to
compute the continuous Haar transform directly, but also to do it quickly. This
is achieved by massively pruning the transform tree and thus dramatically
decreasing the computational load when the number of vertices is small, as is
the case for VLSI layouts. We call this new algorithm the pruned continuous
Haar transform. We implement this algorithm and show that for patterns found in
VLSI layouts the proposed algorithm was in the worst case as fast as its
discrete counterpart and up to 12 times faster.Comment: 4 pages, 5 figures, 1 algorith
Optimal load shedding for microgrids with unlimited DGs
Recent years, increasing trends on electrical supply demand, make us to search for
the new alternative in supplying the electrical power. A study in micro grid system
with embedded Distribution Generations (DGs) to the system is rapidly increasing.
Micro grid system basically is design either operate in islanding mode or
interconnect with the main grid system. In any condition, the system must have
reliable power supply and operating at low transmission power loss. During the
emergency state such as outages of power due to electrical or mechanical faults in
the system, it is important for the system to shed any load in order to maintain the
system stability and security. In order to reduce the transmission loss, it is very
important to calculate best size of the DGs as well as to find the best positions in
locating the DG itself.. Analytical Hierarchy Process (AHP) has been applied to find
and calculate the load shedding priorities based on decision alternatives which have
been made. The main objective of this project is to optimize the load shedding in the
micro grid system with unlimited DG’s by applied optimization technique
Gravitational Search Algorithm (GSA). The technique is used to optimize the
placement and sizing of DGs, as well as to optimal the load shedding. Several load
shedding schemes have been proposed and studied in this project such as load
shedding with fixed priority index, without priority index and with dynamic priority
index. The proposed technique was tested on the IEEE 69 Test Bus Distribution
system
Real-time portable system for fabric defect detection using an ARM processor
Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection. Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications. In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations
Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
Processing of digital images is continuously gaining in volume and relevance,
with concomitant demands on data storage, transmission and processing power.
Encoding the image information in quantum-mechanical systems instead of
classical ones and replacing classical with quantum information processing may
alleviate some of these challenges. By encoding and processing the image
information in quantum-mechanical systems, we here demonstrate the framework of
quantum image processing, where a pure quantum state encodes the image
information: we encode the pixel values in the probability amplitudes and the
pixel positions in the computational basis states. Our quantum image
representation reduces the required number of qubits compared to existing
implementations, and we present image processing algorithms that provide
exponential speed-up over their classical counterparts. For the commonly used
task of detecting the edge of an image, we propose and implement a quantum
algorithm that completes the task with only one single-qubit operation,
independent of the size of the image. This demonstrates the potential of
quantum image processing for highly efficient image and video processing in the
big data era.Comment: 13 pages, including 9 figures and 5 appendixe
- …