Custom Instruction for NIOS Ⅱ processor FFT implementation for Image processing

机译：用于图像处理的NIOSⅡ处理器FFT实现的自定义指令

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

Image processing can be considered as signal processing in two dimensions (2D). Filtering is one of the basic image processing operation. Filtering in frequency domain is computationally faster when compared to the corresponding spatial domain operation as the complex convolution process is modified as multiplication in frequency domain. The popular 2D transforms used in image processing are Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The common values for resolution of an image are 640×480, 800×600, 1024×768 and 1280×1024. As it can be seen, the image formats are generally not a power of 2. So power of 2 FFT lengths are not required and these cannot be built using shorter Discrete Fourier Transform (DFT) blocks. Split radix based FFT algorithms like Good-Thomas FFT algorithm simplifies the implementation logic required for such applications and hence can be implemented in low area and power consumption and also meet the timing constraints thereby operating at high frequency. The Good-Thomas FFT algorithm which is a Prime Factor FFT algorithm (PFA) provides the means of computing DFT with least number of multiplication and addition operations. We will be providing an Altera FPGA based NIOS II custom instruction implementation of Good-Thomas FFT algorithm to improve the system performance and also provide the comparison when the same algorithm is completely implemented in software.

机译：可以将图像处理视为二维（2D）信号处理。过滤是基本的图像处理操作之一。与复杂的卷积过程被修改为频域的乘法运算相比，与相应的空间域运算相比，频域的滤波计算速度更快。图像处理中使用的流行2D变换是快速傅立叶变换（FFT），离散余弦变换（DCT）和离散小波变换（DWT）。图像分辨率的常用值为640×480、800×600、1024×768和1280×1024。可以看出，图像格式通常不是2的幂。因此，不需要2个FFT长度的幂，并且不能使用较短的离散傅立叶变换（DFT）块来构建。像Good-Thomas FFT算法这样的基于分离基数的FFT算法简化了此类应用程序所需的实现逻辑，因此可以在较小的面积和功耗下实现，还可以满足时序约束，从而在高频下运行。作为素数FFT算法（PFA）的Good-Thomas FFT算法提供了使用最少的乘法和加法运算来计算DFT的方法。我们将提供一个基于Altera FPGA的Good-Thomas FFT算法的NIOS II自定义指令实现，以提高系统性能，并在软件中完全实现相同算法时提供比较。

著录项

来源
《Sensing and analysis technologies for biomedical and cognitive applications 2016》|2016年|987105.1-987105.12|共12页
会议地点 Baltimore MD(US)
作者
Sindhuja Sundararajan; Uwe Meyer-Baese; Guillermo Botella;
展开▼
作者单位

FAMU-FSU, ECE Dept., 2525 Pottsdamer Street, Tallahassee, FL USA-32310;

FAMU-FSU, ECE Dept., 2525 Pottsdamer Street, Tallahassee, FL USA-32310;

Dept. Computer Architecture, Universidad Complutense de Madrid. 28040 Madrid (Spain);

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
DFT; FFT; DCT; DWT; PFA; FPGA; NIOS; Image processing;

机译：DFT； FFT; DCT;载重吨; PFA; FPGA; NIOS；图像处理;

相似文献

外文文献
中文文献
专利

1. Implementation-aware selection of the custom instruction set for extensible processors [J] . Amir Yazdanbakhsh, Mehdi Kamal, Sied Mehdi Fakhraie, Microprocessors and microsystems . 2014,第7期

机译：对可扩展处理器的自定义指令集的实现感知选择
2. Implementation of AES as a Custom Hardware using NIOS II Processor [J] . Meghana Hasamnis, Priyanka Jambhulkar, S.S. Limaye Advanced Computing: an International Journal . 2012,第4期

机译：使用NIOS II处理器将AES实现为自定义硬件
3. Hierarchical Design of an Application-Specific Instruction Set Processor for High-Throughput and Scalable FFT Processing [J] . Guan X., Fei Y., Lin H. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on . 2012,第3期

机译：高通量和可扩展FFT处理的专用指令集处理器的分层设计
4. Custom Instruction for NIOS Ⅱ processor FFT implementation for Image processing [C] . Sindhuja Sundararajan, Uwe Meyer-Baese, Guillermo Botella Conference on sensing and analysis technologies for biomedical and cognitive applications . 2016

机译：用于图像处理的NiosⅡ处理器FFT实现的自定义说明
5. REAL-TIME PROCESSING OF INFRARED IMAGES: ENHANCEMENT AND TARGET DETECTION (TWO-DIMENSIONAL, FFT). [D] . DEVORE, JOHN JACOB. 1984

机译：红外图像的实时处理：增强和目标检测（二维，FFT）。
6. Parallel 2D FFT implementation on FPGA suitable for real-time MR image processing [O] . Limin Li, Alice M. Wyrwicz -1

机译：适用于实时MR图像处理的FPGA并行2D FFT实现
7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

机译：摘要的各个身体部位或器官可被分析以识别在人体内的不同的疾病。指甲分析来识别人体疾病的方法之一。指甲是身体一部分是离心脏最远，因此在最后接受氧气。作为结果，指甲是第一谁表现出人体疾病的症状。指甲可以容易地捕获用于诊断和没有重装或需要使用指甲图像用于疾病诊断，比如在其他测试和扫描过程没有特定的条件。人的指甲提供有关投诉或取决于它们的形状，纹理和色彩在人体内的任何营养失衡有益的信息。在人类中，许多全身性皮肤疾病是可以很容易地通过两个四肢指甲的仔细检查分析。很多指甲病已发现众多潜在系统性疾病的主要症状。在指甲的颜色，质地和形状的变化是许多疾病主要影响指甲的迹象。考虑到所有的指甲的这些性能的系统被提出，用于识别人指甲这样的变化以获得更精确的结果，并毫不费力预测许多疾病用途的数字图像处理（DIP）方法。随着物联网（IOT）的概念，新兴的互联网将生成的报告提供远程，这将帮助用户降低运输工作。由于系统必须处理大量的私人数据，数据的安全性必须得到保证。为了保持数据的机密性，使用Blockchain的概念，它是在数据管理领域的大多数新兴的概念之一。本文包含了数字图像处理的指甲图像，IOT（ThingSpeak云）的使用为数据存储和执行Blockchain的特征提取的执行，以保持固定的系统和盗窃免费。关键词：诠释薄GS（IOT），图像的ERNET PROC essin克，薄型gSpeak，RG乙vavalues，平均数PI XEL vavalues，阵营kchain，哈希密钥。疾病诊断系统：在人类指甲异常

Custom Instruction for NIOS Ⅱ processor FFT implementation for Image processing

摘要

著录项

相似文献

相关主题

期刊订阅