System Generator

El presente trabajo describe el diseño, simulación e implementación en una FPGA de un Codificador de Canal para Wimax, enfocándose en el estándar IEEE 802.16-2004 el cual representa la implementación fija y forma parte de la investigación en el campo de redes de acceso fijo inalámbrico de banda ancha sin línea de vista. El trabajo presenta las principales características usadas en Wimax para la transmisión y recepción además del funcionamiento de cada uno de los bloques usados para la corrección de errores. Este proyecto presenta una implementación en FPGA usando diseño basado en modelo, usando el software System Generator junto a Matlab y Simulink, para poder obtener los datos que nos permitan comprobar el funcionamiento del diseño propuesto de acuerdo a las especificaciones del estándar de Wimax y además analizar la capacidad de corrección de errores del sistema.

El presente trabajo muestra el desarrollo de una plataforma amigable con el usuario para la ensenanza de conceptos fundamentales de la capa fisica de una red LTE Release 8 (R8), basada en las herramientas de MATLAB/SIMULINK. La plataforma busca ser un aporte a la formacion de un Ingeniero en Telecomunicaciones tomando como base los topicos de la asignatura de Redes de Nueva Generacion que se dicta en la Universidad de las Fuerzas Armadas ESPE. Una vez definidos los temas se procedio a implementar el codigo necesario para obtener como resultados graficas del BER correspondientes a diferentes modelos de canal y opciones de modulaciones, codificacion de canal, ecualizacion, multiple input multiple output (MIMO), asi como observar mallas de recursos y el espectro de senales para que de esta manera que el usuario final pueda realizar un analisis comprensivo y analitico de lo que sucede en la capa fisica de LTE R8.

The methodologies presented in scientific literature to calculate the threshold of an image binarization process do not present good results for all types of images. Additionally, the hardware implementations do not consider the FPGA resources that are used in other processing phases. Thus, the method proposed in this work aims to present good results in the binarization process with under-resourced area of FPGA. Therefore, this paper proposes the FPGA implementation of a threshold algorithm used in the process of image binarization by simple mathematical calculations. The implementation only needs one image iteration and its processing time depends on the size of the image. The threshold values of different images obtained through the FPGA implementation are compared with those obtained by Otsu's method, showing the differences and the visual results of binarization using both methods. The hardware implementation of the algorithm is performed by a model-based design supported by the MATLAB ® /Simulink ® and Xilinx System Generator ® tools. The results of the implementation proposal are presented in terms of resource consumption and maximum operating frequency in a Spartan-6 FPGA-based development board. The experimental results are obtained in co-simulation system and show the effectiveness of the proposed method.

In Mathematic, a QR decomposition, also known as a QR factorization or QU factorization, is a decomposition of a matrix A into a product A = QR of an orthonormal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares (LLS) problem and is the basis for a particular eigenvalue algorithm, the QR algorithm.
A good way to write an algorithm intended for a fixed-point target is to write it in MATLAB using built-in floating-point types so we can verify that the algorithm works. When we refine the algorithm to work with fixed-point types, then the best thing to do is to write it so that the same code continues working with floating-point.

In MATLAB we can explore and manipulate many functions to deal with QR factorization and in this article, we have use various coding methods that can be applied across systems. The significant design
patterns used are the following:
• Data Type Independence: the algorithm is written in such a way that the MATLAB code is independent of data type, and will work equally well for fi, double, and single.
• Overflow Prevention: method to guarantee not to overflow. This demonstrates how to prevent overflows in fixed-point.
• Solving Systems of Equations: method to use computational efficiency. Narrow your code scope by isolating what you need to define.
The main part in this example is an implementation of the QR factorization in fixed-point arithmetic using CORDIC for the Givens
rotations. The algorithm is written in such a way that the MATLAB code is independent of data type, and will work equally well for fixed-point, double-precision floating-point, and single-precision floating-point.

This paper proposes the increases throughput and reduces latency by using hybrid QR decomposition with pipelined and parallel design. The 4G wireless standards require MIMO systems with large antenna configurations, high mobility and, high data rates. In MIMO systems, the main challenge is to implement a QR decomposition process that efficiently utilizes hardware resources. By using MIMO technology in LTE-Advanced to achieves the highest detection throughput of 1Gbps data rates in downlink side. The proposed QR decomposition method is synthesized on Xilinx XC6VLX550T-2FF1759. Test results for the FPGA implementation, shows that the proposed design achieves the lowest latency of 100ns at 300MHz and reduces area.

This paper proposes the increases throughput and reduces latency by using hybrid QR decomposition with pipelined and parallel design. The 4G wireless standards require MIMO systems with large antenna configurations, high mobility and, high data rates. In MIMO systems, the main challenge is to implement a QR decomposition process that efficiently utilizes hardware resources. By using MIMO technology in LTEAdvanced to achieves the highest detection throughput of 1Gbps data rates in downlink side. The proposed QR decomposition method is synthesized on Xilinx XC6VLX550T-2FF1759. Test results for the FPGA implementation, shows that the proposed design achieves the lowest latency of 100ns at 300MHz and reduces area. Keywords–FPGA, Hybrid QR decomposition, K-best detector, LTE-A, MIMO (Multiple Input Multiple Output).

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

Simple hardware architecture for implementation of pairwise Support Vector Machine (SVM) classifiers on FPGA is presented. Training phase of the SVM is performed offline, and the extracted parameters used to implement testing phase of the SVM on the hardware. In the architecture, vector multiplication operation and classification of pairwise classifiers is designed in parallel and simultaneously. In order to realization, a dataset of Persian handwritten digits in three different classes is used for training and testing of SVM. Graphically simulator, System Generator, has been used to simulate the desired hardware design. Implementation of linear and nonlinear SVM classifier using simple blocks and functions, no limitation in the number of samples, generalized to multiple simultaneous pairwise classifiers, no complexity in hardware design, and simplicity of blocks and functions used in the design are view of the obvious characteristics of this research. According to simulation results, maximum frequency of 202.840 MHz in linear classification, and classification accuracy of 98.67% in nonlinear one has been achieved, which shows outstanding performance of the hardware designed architecture.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

Simple hardware architecture for implementation of pairwise Support Vector Machine (SVM) classifiers on FPGA is presented. Training phase of the SVM is performed offline, and the extracted parameters used to implement testing phase of the SVM on the hardware. In the architecture, vector multiplication operation and classification of pairwise classifiers is designed in parallel and simultaneously. In order to realization, a dataset of Persian handwritten digits in three different classes is used for training and testing of SVM. Graphically simulator, System Generator, has been used to simulate the desired hardware design. Implementation of linear and nonlinear SVM classifier using simple blocks and functions, no limitation in the number of samples, generalized to multiple simultaneous pairwise classifiers, no complexity in hardware design, and simplicity of blocks and functions used in the design are view of the obvious characteristics of this research. According to simulation results, maximum frequency of 202.840 MHz in linear classification, and classification accuracy of 98.67% in nonlinear one has been achieved, which shows outstanding performance of the hardware designed architecture.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

En este artículo se presenta la utilización de Ma­trices Lógicas Programables por Campo (FPGA) para la implementación de un sistema de evalu­ación de redes neuronales artificiales (RNA), y la aplicación de reconocimiento de patrones de imá­genes de caracteres alfabéticos, en el que se utili­zan diferentes modelos de redes neuronales. Esta investigación se desarrolló para evaluar el desem­peño de las redes neuronales implementadas en la FPGA y para explorar y analizar las caracter­ísticas de funcionamiento de la FPGA Spartan-3 XCS200. El proyectó se realizó en cuatro fases, las cuales son: establecer los modelos de redes neuronales, desarrollo de una plataforma de en­trenamiento de RNA utilizando Labview, diseño, implementación y verificación de los modelos de RNA, y por último, se establecieron los parámet­ros y se realizó la evaluación del desempeño de las RNA implementadas en la FPGA Spartan-3 XCS200.

This paper presents performance evaluation of two implementations of an equalizer: a time domain equalizer (TDE) based on the Least Mean Squares (LMS) algorithm and a frequency domain equalizer (FDE) based on the Fast LMS algorithm. The comparison between the two algorithms is based on the computational complexity and resources. The computational complexity of the two algorithms is analyzed by simulation of the TDE and FDE at at two levels of abstraction: the design specification based on floating point arithmetics using Simulink, and the design implementation based on fixed point arithmetics using Xilinx's System Generator tool. The models are used to measure both floating-point and fixed-point signal-to-noise ratio (SNR) errors based on the two algorithms and provide error estimation for the design specification and design implementation. We analyze the resources used in the implementation of the two algorithms by providing FPGA implementations in System Generator. Our analysis shows that the FDE is more efficient in terms of computational complexity and resources.

This paper presents the implementation of Neural Networks in a programmable logic device such as the FPGA, the aim is to develop a model in Sysgen able to estimate the rotor resistance in induction motors. To create a neuron in Sysgen is from the standard model of an artificial neuron, which is the sum of weights minus the threshold, all multiplied by an activation function, in this case the activation function was used for Tansig Purelin hidden layer to output layer. The neural network uses decimal data input/ output, and the FPGA is digital, therefore Sysgen was designed in a fitting capable of handling binary data on the card, for this we used a Digital-Analog Converter, and was created as suppress the number of pins used in the FPGA, designing a stage of registers used to store the desired data and thus send it to one of the inputs of the neural network. The data frame becomes progressively each record is used to store the decimal value present at the output of Digital-Analog converter and each time you change the data logging is activated for the input of the neural network.

The communication industry field is mainly focused by high data transfer and more channel capacity in mobile communication. VLSI technology is used to modify any type digital based hardware architecture and to reduce the hardware system power, speed and complexity level. The filter process is mainly used to DSP and DIP real world application. The filter process is to remove the noise in original signal or image. So the filter architecture optimized process, to reduce the filter processing time and to increase the performance. Adaptive digital filters find wide application in several digital signal processing (DSP) areas. FIR filter architecture is used to effectively remove the noise in received channel data bits and to require less circuit complexity.The existing system frequency transformation based filters (FT filters) provide an absolute control over the cutoff frequency. However, the cutoff frequency range of the FT filters is limited. The second-order frequency transformations combined with coefficient decimation technique based filter (FTCDM filter) has wider range compared with the FT filter. But this method have some limitation. So we use the discrete cosine transform (DCT) modulation based low pass filter transformation process. Proposed system is to design a 18-band DCT transform signal denoising architecture simulink design. This simulink design is to consist the XILINX based digital architecture block. Discrete cosine transform (DCT) modulation are utilized to generate a uniform 18-band filter bank first, and then all elements of are replaced by all-pass filters to obtain a non-uniform filter bank. A fast recursive structure and variable-length algorithm is further developed to efficiently accomplish DCT modulation. Proposed system is to improve the output signal reconstruction effectively. Proposed system is to reduce the latency level and to increase the application system speed level.

In linear algebra, Householder transformation is a linear transformation that is widely used in QR decomposition and finds application in communication systems, image and signal processing like adaptive beam forming, MIMO systems, channel equalization, echo cancellation etc. This paper describes the realization of available hardware architectures for the computation of Householder transformation based QR decomposition. Simulink supplied by Mathworks and System Generator developed by Xilinx ISE Design suite 14.4 is used for the realization of hardware architectures.

This article presents a design methodology for designing an artificial neural network as an equalizer for a binary signal. Firstly, the system is modelled in floating point format using Matlab. Afterward, the design is described for a Field Programmable Gate Array (FPGA) using fixed point format. The FPGA design is based on the System Generator from Xilinx, which is a design tool over Simulink of Matlab. System Generator allows one to design in a fast and flexible way. It uses low level details of the circuits and the functionality of the system can be fully tested. System Generator can be used to check the architecture and to analyse the effect of the number of bits on the system performance. Finally the System Generator design is compiled for the Xilinx Integrated System Environment (ISE) and the system is described using a hardware description language. In ISE the circuits are managed with high level details and physical performances are obtained. In the Conclusions section, some modifications are proposed to improve the methodology and to ensure portability across FPGA manufacturers.