Design Of Processing Algorithm Research Articles

Distributed 3D Environment Design System Based on Color Image Model

In order to improve the effect of distributed 3D interior design, a distributed 3D interior design method based on color image model is proposed in this paper. In this paper, the author uses the distributed feature information fusion method to construct the color image model of the distributed 3D interior design, and carries out edge contour detection and feature extraction for the distributed 3D interior spatial distribution image. The RGB color decomposition method is used to decompose the color pixel features of the three-dimensional indoor spatial distribution image. Combined with the three-dimensional point cloud feature reorganization method, the color space reconstruction of the distributed three-dimensional interior design is realized, and the optimal combination of color features of the distributed three-dimensional interior design is realized. Based on the design of image and color processing algorithm, the development and design of distributed 3D interior design system is carried out based on virtual reality and visual simulation technology. The 3D modeling of distributed 3D interior design is carried out using 3ds max, and the indoor hierarchical structure design is realized using the modeling software Multigen Creator. The experimental results show that, according to the color information fusion results, the distributed 3D interior design optimization is realized. Compared with the traditional manual design method, the root mean square error of this method is greatly reduced from 0.232 to 0.023, and the time cost is 82.8% faster. This method has a better visual effect and strong feature expression ability. Conclusion. The visual effect of distributed 3D interior design with this method is good, the error is small, and the visual expression ability is strong.

Design and Implementation of a Subnanometer Heterodyne Interference Signal Processing Algorithm with a Dynamic Filter.

In this study, a subnanometer heterodyne interference signal processing algorithm with a dynamic filter is proposed. The algorithm can effectively reduce the measurement error caused by the noise introduced in the optical path and circuit. Because of the low signal−to−noise ratio of the measurement signal, a dynamic filter with variable coefficients is designed. The role of the bi−quadrature lock−in amplifier algorithm in the problem of different amplitudes among the measurement signal, reference signal, and uncertainty of the frequency difference of the dual−frequency laser is analyzed. With the aid of the heterodyne interferometry platform, the error in the solution results of the proposed algorithm and the conventional algorithm is compared. The results indicate that the maximum deviation of the phase increment of the algorithm does not exceed 6 mrad, the single−cycle phase difference can be subdivided by 1024, and the system resolution reaches 0.15 nm.

Research on the Design of Digital Signal Processing Algorithm Based on Spark Parallel Calculation

Digital signal processing as a key and difficult point of network technology research and development, currently commonly used content such as LabVIEW. But from a practical point of view, while these techniques can be used to process real-time signals, they can’t handle historical offline data. The Spark parallel computing studied in this paper can be used to process offline signals. Therefore, on the basis of understanding the development trend of Spark parallel computing framework, the distributed Mallat algorithm is analyzed based on Spark parallel computing engine, and the application performance of the corresponding algorithm is verified.

Research and Hardware Design of Image Processing Algorithm Based on FPGA

With the continuous development of computer technology and microelectronics technology, image processing technology is constantly being improved, especially real-time image processing technology has been studied by a large number of scholars. This article aims to achieve the purpose of FPGA-based fast image processing through the research of FPGA’s fast image processing algorithm. Based on the advantages of FPGA high-speed hardware computing and parallel computing, this paper proposes a design based on FPGA parallel dual template strategy, and designs a comparative experiment to verify. Obtain objective evaluation indexes of image processing results through experiments, and analyze the data results. The experimental results show that: the implementation of the mean filter algorithm based on FPGA parallel dual templates, the time required is about half that of the mean filter algorithm based on FPGA single templates. Relative to the reference Matlab software programming method to achieve 678 times performance improvement. It shows that the realization method of the mean filter algorithm based on FPGA parallel dual template is feasible. Give full play to the advantages of FPGA parallel processing data, improve the processing speed of the image algorithm module.

Design and Implementation of 5-D IIR Depth Velocity Filters for Light Field Video Processing

The design and hardware implementation of a low-complexity signal processing algorithm is proposed for real-time depth-velocity filtering in 5-D light field videos (LFVs). The proposed design is based on a stable 5-D infinite impulse response digital filter having three cascaded sections, each synthesized using the concept of multidimensional passive network resonance. A novel semi-systolic hardware implementation is proposed. Each section of the filter is implemented and tested on a Xilinx Virtex-7 FPGA platform using MATLAB-based hardware co-simulation with both synthetic and real LFV signals. A real-time processing throughput of 467 LFV frames/s is implied with each section of the filter operating at 204, 164, and 115 MHz for input LFV frames of size $9 \times 9 \times 220 \times 360$ .

Design of LIDR Monitoring System Based on GPRS

Through the analysis of processing 3D point cloud data obtained by laser radar and establishment of the point cloud model to calculate the volume and displacement to monitor the particular scene in accordance with the detection strategy of the dangerous situation, This article wants to develop a set of all-weather, real-time early monitor system, in order to timely risk information publish, reduce the losses caused by disasters. The system uses a modular design approach, which is divided into the information collection module, the information processing module, the analysis of early warning modules and information transmission module. The paper details the system design, and application in the design and implementation of technology and point cloud data processing algorithm. The system use serial programming techniques for laser radar data and control PTZ, and multi-threading technology to improve the efficiency of the serial communication and resolve the problem of multi-serial real-time data collection.

Design of Nonuniform Linear Antenna Array Geometry and Signal Processing Algorithm for DOA Estimation of Gaussian Sources

Abramovich, Yuri I., and Spencer, Nicholas K., Design of Nonuniform Linear Antenna Array Geometry and Signal Processing Algorithm for DOA Estimation of Gaussian Sources, Digital Signal Processing10 (2000), 340–354.This paper discusses the problem of direction-of-arrival (DOA) estimation for Gaussian sources that have arbitrary correlation: from independent to fully correlated. For independent sources, the antenna array design is governed by two competing considerations: maximum aperture, which inclines toward increasing sparsity for a given number of array sensors, and identifiability, which tends to exclude extreme sparsity. For fully correlated sources, these two competing criteria are augmented by a third which allows for the initialization of DOA estimation by the generalized spatial smoothing (GSS) technique. The maximum number of fully correlated sources is in turn an important factor in the GSS algorithm and subsequent array geometry design. We present a geometry optimization technique that permits accurate DOA estimation of arbitrarily correlated sources.

Frequency Acquisition in a Signal with Few Sinusoids Using Effective Fourier Transform

The evaluation of discrete Fourier transforms (DFT) in a real time is a common problem in various technical areas. Nowadays, research in technology offers more and more powerful tools not only for DFT evaluation, but for many signal processing algorithms as well. However, many applications still need either a faster or cheaper solution. In spite of signal processors, where multiplication is usually a one-cycl-operation, in VLSI design of signal processing algorithm, the number of multiplications is one of the important factors for the effective complexity limit of the algorithms. This paper describes a novel algorithm (Effective Fourier Transform, EFT) for an approximative computation of a DFT without multiplication. The algorithm is based on the generation of a discrete harmonic function. Considering some constraints for the frequency of the signal, an arithmetic shift can be used instead of the multiplication by \cos(\Omega). Columns of the DFT- matrix can be approximated by the reorganized samples of the harmonic generator. Considering addition and arithmetic shifts as basic operations, EFT is faster than the classical \lq\lq butterfly" fast Fourier transform. An error analysis of the algorithm depends on the speed limitation. Several factors, such as the length of the DFT and number of sinusoids in an analyzed spectrum have an effect on the error analysis. Maximum error cca 2 \% of the spectral magnitude is guarantied for the worst case. For well- separated sinusoids in the spectrum and for some \lq\lq magic" lengths of EFT, a maximum error cca 0.1\% can be achieved. For statistical evaluation of the spectrum EFT is even more suitable, as one part of the algorithm can be done only once for several EFT computations.