1,721,011 research outputs found
Design and Implementation of Single Phase Inverter Using FPGA
No full textMuhammad Imran Ahmad and his team from School of Computer & Communication Engineering won silver for Design and Implementation of Single Phase Inverter using FPGA. ITEX 2007, 18th - 20th May 2007 was held at Kuala Lumpur Convention Centre (KLCC), Kuala Lumpur.The product is a single phase inverter that convert DC voltage to a pure sine wave 200 volt AC with low harmonics distartion which is less than 3%, thus suitable for use in complex electrical equipment and frequency sensitive equpments
Multilevel Inverter Control Circuit Based on DSP-FPGA Coprocessor
No full textEkspo ini berlangsung di Dewan Sek. Men. Putra, Kangar, Perlis.This project design to convert DC voltage to pure sine wave AC voltages. The output consists of Low Harmonics Distortion and produce high current
Face recognition using holistics features of low frequency infomation
No full textAccess is limited to UniMAP community.Face recognition has been getting the most attention from researchers in biometrics. Since the introduction of biometrics, it tried to find a process identified by comparing the current biometric pattern to the database. Here is a function of behavior in addition to the
physiological much of which work in the program as an example of biometric fingerprint,
iris scanning, signature, hand geometry and voice/speech. Man or woman can be separated as proof of identification in addition to the recognition will depend on your circumstances of the request. Some work on facial recognition has been successful method to identify facial features or remotely using a template that includes some of the area. A holistic technique used to identify characteristics or face geometry was introduced. Characterization advance achieved by random sampling of selected properties of the image pixels. From this information, we developed a data set corresponding to the low frequency values. The facial recognition systems for personal identification and validation using Principle Component Analysis (PCA) are among the most common technique for feature extraction technique used in face recognition. The test results in a database interface Olivetti Research Laboratory (ORL) to produce interesting results from the point of view of the recognition success, rate, and the robustness of face recognition algorithms
Gait recognition using principle component analysis implemented on DSP Processor
No full textThis research focus on the development of an automatic human identification system
using gait sequence images. Human identification is widely used in computer vision applications such as surveillance system, criminal investigations and human-computer interaction. Many identification approaches have shortcomings thus they require subject cooperation and sensitive to environmental and physiological changes. They also have high computational cost and are time consuming thus difficult to implement in
hardware. Gait sequence consists of non-stationary data and can be modeled using a
statistical learning technique. The proposed method consists of three different stages.
The pre-processing stage computes the average silhouette images to capture the
important information and get a better representation for gait silhouette data. Then a
principle component analysis (PCA) technique is applied on the average silhouette to
extract the important gait features and reduce a dimension of gait data. A linear
projection method used in this stage is able to reduce redundant features and remove
noise from the gait image. Furthermore, this approach will increase a discriminating
power in the feature space when dealing with low frequency information. Low
dimensional feature distribution in the feature space is assumed to be Gaussian, thus the
Euclidean distance classifier can be used in the classification stage. The proposed
algorithm is a model-free based which uses gait silhouette features for the compact gait
image representation and a linear feature reduction technique to remove redundant
information and noise. The proposed algorithm has been tested using a benchmark
CASIA dataset. The experimental results show that the best recognition rate is 90%
when the image is represented using 500 PCA coefficients. Low number of PCA
coefficients will give a possibility for the Euclidean distance classifier to be
implemented in hardware such as DSP processor. The implementation of the proposed
algorithm using the DSP-based processor achieved better performance in term of
computational time compared to the PC-Based processor with a ratio of 0.5 seconds
Face recognition system using DCT features implemented on DSP processor
No full textFace recognition is a challenge because the faces always change due to facial
expression, direction, light, and scale. Furthermore, it needs good computing techniques for recognition in order to reduce the system’s complexity. Our approach focuses on the local feature extraction in the frequency domain. DCT was proposed as the feature extraction algorithm for face recognition, which captures the important features in the
face image and at the same time reduces the feature space. PCA then performs the
feature reduction of the extracted image and produces a small size of feature vector. The
propose method can reduce data dimension in feature space. The classification is done
by using the Euclidean distance between the projection test and projection train images.
The algorithm is tested using DSP processor and achieve a same performance with PC
based. The extensive experimentations that have been carried out upon standard face
databases such as ORL shows that significant performance is achieved by this method,
which is 98.5% for best selected test image and 95% for the worst selected test image.
Besides that, execution time is also measured, whereby to recognize 40 people, the
system only requires 0.3313 second. The proposed method not only offers
computational savings, but is also fast and has a high degree of recognition accuracy
Palmprint recognition using eigen-palm image implemented on DSP processor
No full textThis study focuses on the development of a human identification system using eigenpalm
images. Human identification based on biometric technology is extensively used
in several applications, such as access control and criminal investigation. The proposed
method consists of three main stages. The preprocessing stage computes the palmprint
images to capture important information and produce a better representation of
palmprint image data. The second stage extracts significant features from palmprint
images and reduces the dimension of the palmprint image data by applying the principal
component analysis (PCA) technique. A linear projection method is used in this stage to
reduce redundant features and remove noise from the palmprint image. Furthermore,
this approach increases discrimination power in the feature space. The Euclidean
distance classifier is used in the classification stage, which is the third stage. The
proposed method is tested using a benchmark PolyU dataset. Experimental results show
that the best achieved recognition rate is 97.5% when the palmprint image is resized
with 0.2 resizing scale and represented using 34 PCA coefficients. The raw data
projection and Euclidean distance classifier can be implemented on a digital signal
processor (DSP) board. Implementing the proposed algorithm using the DSP board
achieves better performance in computation time compared with a personal computerbased
system which make the system 47.2% faster
Electronic dictionary for Herb’s plant
No full textBiotechnology is recently been given particular attention to the researchers in
Malaysia. The growth of biotechnology field is increased robustly nowadays. Among the species, the herb’s species is very potential in biotechnology research area for food and medicine industry. Malaysia is enriched with a variety of herb’s species. But, most of these precious plant species data are not well managed. Some of the database is even stored in a PC, which is very bulky and power hungry. So, it is not suitable for the Biotechnology researches in the field. To overcome this problem, a high performance device of electronic dictionary for herb’s plant is designed. The device is designed by using FPGA. The FPGA is suitable because of its high speed and parallel data
processing. It is also definitely suitable for mobile application. The control unit of
electronic dictionary consists of an FPGA chip as a main processing unit, a memory
module, LCD panel as a output display unit and a keypad as the input. VHDL is used to
define the FPGA functional unit in Quartus II. The new devices are helpful and useful
to simplify control and modify operating speed of a microprocessor managing its
peripheral devices
Floating point multiplication unit using FPGA
No full textAccess is limited to UniMAP community.Field-programmable Gate Array (FPGA) is a semiconductor device containing programmable logic components called "logic blocks", and programmable interconnects. Logic blocks can be programmed to perform the function of basic logic gates such as AND, and XOR, or more complex combination functions such as decoders or simple mathematical functions. The architecture of floating point multiplication unit is designed using Cyclone FPGA chip. Floating point numbers are represented in IEEE 754 format which consists of 8 bits biased exponent, 23 bits fraction and sign bit. The suitability of FPGA as design platform is studied and performance of multiplication process is also observed for this project. Performance of multiplication process in various design aspects is done to achieve the objectives of this project. Design of architecture of floating point multiplication unit is done by using VHSIC hardware description language (VHDL) and Quartus II software using Altera UP3 Board which will be used as a simulation and synthesis tools. This project shows an example on how Floating Point Multiplication Unit Using FPGA is conducted using Quartus II software and VDHL
Information fusion of face and palm-print multimodal biometric at matching score level
No full textMultimodal biometric systems that integrate the biometric traits from several modalities
are able to overcome the limitations of single modal biometrics. Fusing the information at the middle stage by consolidating the information given by different traits can give a better result due to the richness of information at this stage. In this thesis, an information fusion at matching score level is used to integrate face and palm-print
modalities. Three types of matching score rule are used which is sum, product and
minimum rule. A linear statistical projection method based on the principle component
analysis (PCA) is used to capture the important information and reduce feature
dimension in the feature space. A fusion process is performed using matching score
computed using Euclidean distance classifier. The experiment is conducted using a
benchmark ORL face and PolyU palm-print dataset to examine the recognition rates of
the propose technique. The best recognition rate is 98.96% achieved by using sum rule
fusion method. Recognition rate can also be improved by increasing number of training
images and number of PCA coefficients
Palmprint recognition using local features
No full textAccess is limited to UniMAP community.There was a period of time when biometrics had been viewed as the technology
for the feature. It has showcased prominently in variety of science fiction films being on
sophisticated protection measure that is used to protect important documents or files,
and propreties etc. Today, it is not far from reality with the help of overly busy
innovation of technology. Biometrics will be progressively being used for protected
authentication of people or individual’s as well as creating its existence felts within
human lives. By using an individual’s physical or behavioral characterisctics to
recognize these individuals. It is a complicated function of the people’s protection need,
simplicity of use as well the size of the organization by the decision which that
biometric is employed. Due to the stability and unique characteristic, palmprint is
probably the comparatively a brand-new physical biometrics. The rich texture details of
information of palmprint provides on the effective means within personal or individuals
identification. The main visual section of the human brains is in charge of making the
foundation of three-dimensional chart of visual space and extracting features concerning
the type and positioning of the objects based on psycho-physiology research. The
fundamental design can be indicated as linear superposition of basis function.
Specifically, this particular concepts named Principal Component Analysis (PCA)
motivated us to be able to implement a linear projection technique to extract the
palmprint consistency texture features. In this paper has an overview and methods
utilizing for capturing an image, processing an image, pre-processing, verification of
algorithm, algorithm specifically created for real time palmprint recognition in large
database and measures regarding safeguarding palmprint systems along with users
privacy
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