43 research outputs found
Implementation of FPGA-based artificial neural network for character recognition
No full textMaster of Science (Embedded System Design Engineering)Artificial Neural Networks (ANN) are non-linear applied math knowledge data modeling
tools, usually used model advanced relationships between inputs and outputs or to seek out patterns in data. A generic hardware primarily based ANN is planned and executed
using VHDL coding. This project may be seen as a place to begin for learning ANN. It
explores in approach a hardware-based application of ANN employs FPGA. The sixteen
toggle switches are given as input while the end product is exhibited on the LCD display.
This classifier is trained to identify letters on a 4x4 binary grid filled by a user through 16
toggle switches. The most probable class suggested by the ANN is displayed on an LCD
screen. To demonstrate the practicality of FPGA execute of ANN, the ANN trained to
acknowledge twenty English and nine Arabic character patterns on a 4x4 grid. In structural
of ANN, the used of three-layer is implemented entirely with 32-bit single exactitude
floating purpose arithmetic to ensure flexibility and accuracy for its wide selection of
applications. The resulting design file is programmed into the Altera Cyclone II FPGA on
the Altera DE2 development and education board. The design also includes a training
supervisor that trains the ANN recognized the total of 29 English and Arabic alphabet
predefined characters. The result is promising as the ANN is able to recognize all
characters defined to training characters patterns. Each alphabet is tested in 20 English
alphabet and 9 Arabic alphabet, after implementation, are done and performance issues of
the design are analyzed. The output gives good results, and finally this project shows the
flexibility and also the endless chance of hardware primarily based implementation of
ANN , the achievement of recognition rate for alphabet English and Arabic character are
76.92% and 32.14% respectively
Implementation of FPGA-based artificial neural network for character recognition
Get PDFArtificial Neural Networks (ANN) are non-linear applied math knowledge data modeling
tools, usually used model advanced relationships between inputs and outputs or to seek out patterns in data. A generic hardware primarily based ANN is planned and executed using VHDL coding. This project may be seen as a place to begin for learning ANN. It explores in approach a hardware-based application of ANN employs FPGA. The sixteen
toggle switches are given as input while the end product is exhibited on the LCD display.
This classifier is trained to identify letters on a 4x4 binary grid filled by a user through 16 toggle switches. The most probable class suggested by the ANN is displayed on an LCD screen. To demonstrate the practicality of FPGA execute of ANN, the ANN trained to
acknowledge twenty English and nine Arabic character patterns on a 4x4 grid. In structural of ANN, the used of three-layer is implemented entirely with 32-bit single exactitude floating purpose arithmetic to ensure flexibility and accuracy for its wide selection of applications. The resulting design file is programmed into the Altera Cyclone II FPGA on the Altera DE2 development and education board. The design also includes a training supervisor that trains the ANN recognized the total of 29 English and Arabic alphabet predefined characters. The result is promising as the ANN is able to recognize all
characters defined to training characters patterns. Each alphabet is tested in 20 English
alphabet and 9 Arabic alphabet, after implementation, are done and performance issues of
the design are analyzed. The output gives good results, and finally this project shows the
flexibility and also the endless chance of hardware primarily based implementation of
ANN , the achievement of recognition rate for alphabet English and Arabic character are
76.92% and 32.14% respectively
Inventory Control Management System
Get PDFThis project is aimed at developing an inventory control management system to allow the operator controlling and updating the flow of inventory in any organization. This
system is developed by using software Visual Basic version 6.0 integated with Microsoft Access 2000 and Seagate Crystal Report version 6.0. The Crystal Seagate Report is chosen because it is easy to developed and also its format more systematic compared to other report Format. Overall the developed system is increase the user's confidence because it is easy to used. Further works such as to upload the system to the Internet could be performed to improve the performance of the system
Relationship between controllable process parameters on bump height in ENIG
No full textLink to publisher's homepage at http://www.ttp.net/This paper reports the factors that affect the bump height in electroless nickel immersion gold (ENIG) and their interrelation between each other. Bump height is a critical issue that needs to be investigated because a certain quality and requirements of bump height needs to be achieved prior to reflow oven soldering process. A total of four controllable process variables, with 16 sets of experiments were studied using a systematically designed design of experiment (DOE). The result suggests that the electroless nickel bath time has the most significant effect on the formation on bump height and consequently provide larger area for conductivity
A New Perceptual Mapping Model Using Lifting Wavelet Transform
Get PDFPerceptual mappingapproaches have been widely used in visual information processing in multimedia and internet of things (IOT) applications. Accumulative Lifting Difference (ALD) is proposed in this paper as texture mapping model based on low-complexity lifting wavelet transform, and combined with luminance masking for creating an efficient perceptual mapping model to estimate Just Noticeable Distortion (JND) in digital images. In addition to low complexity operations, experiments results show that the proposed modelcan tolerate much more JND noise than models proposed befor
Energy efficient segmentation-link strategies for transparent IP over WDM core networks
No full textLink to publisher's homepage at http://www.jocm.us/Recent developments in Optical IP networks have heightened the need for reduction on power consumption via so called green photonics and concepts known as green networking. Adhering to these principles we proposed a novel energy savings approach which can be applied to optical IP networks and is based on so called “hibernation mode with segmentation link” technique. The hibernation mode technique is designed to help to reduce power consumption by sleep the network links and can help to optimise network overall energy usage by taking advantage of the hibernation algorithms we have developed. The proper implementation over the network infrastructure will save energy, operating cost to network operators, and while at the same time will also deliver reduced carbon footprint. In this paper, we show design model for developing energy efficient Segment-Link approach. In this scheme, the improvised energy saving techniques focus on selective sleeping of optical core fibre links, following which the Routing Wavelength Assignment algorithm based on GMPLS control plane are investigated. Simulation models and results for the proposed scheme are evaluated in both the fibre link only and segmentation-link schemes scenarios. The results shows that a significant amount of energy can be saved if appropriate “segmentation-link” mechanisms using optical bypass and traffic grooming is invoked
FPGA implementation for GMM-based speaker identification
Get PDFLink to publisher's homepage at http://www.hindawi.com/In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors such as Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal. A well-known statistical modelling process, the Gaussian Mixture Model (GMM), then models the distribution of each speaker's MFCCs in a multidimensional acoustic space. The GMM-based speaker identification system has features that make it promising for hardware acceleration. This paper describes the hardware implementation for classification of a text-independent GMM-based speaker identification system. The aim was to produce a system that can perform simultaneous identification of large numbers of voice streams in real time. This has important potential applications in security and in automated call centre applications. A speedup factor of ninety was achieved compared to a software implementation on a standard PC
SAS-HRM: Secure Authentication System for Human Resource Management
Get PDFTo guarantee data confidentiality and information sensitivity, human resource management requires secure systems. In the field of authorization and dependability in recognizing and identifying persons, facial recognition has grown in importance. In this research, a secure authentication system is proposed based on biometric aspects of the user's face and identifying it using the CNN classification model is provided to give access to human resource management and update data. The system is divided into four major stages: First, set up the system environment, beginning with smart cards, card readers, Arduino, and so on. Second, after undergoing pre-treatment steps, the facial characteristics are extracted using LDA. Third, create a high-accuracy CNN model to recognize and classify the user's face among the system's users. Finally, the user is allowed to enter the system and update his information. When compared to the accuracy of classification using machine learning techniques with a CNN proposed model, the accuracy of the model with LDA was up to 100%. K-NN has 91%, while TD has 94%
Finite Element Analysis on Sn-3.9Ag-0.6Cu and Sn-3.5Ag-0.7Cu Using Different Shearing Height
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