Design of Gamma Spectrum Data Acquisition and Processing System Based on ARM / DSP

Research for detecting or obtaining radionuclide by gamma energy spectrum data acquisition and process system is one of the key issues about intelligent measurement of gamma-ray spectrum. For this reason, a software and hardware implementation schematic design based on ARM ( Advanced RISC Machines) + DSP ( Digital Signal Processor) architecture for gamma energy spectrum data acquisition and processing system is proposed. The paper discusses in detail some key technologies such as communication interface design between microcontroller ARM and digital signal processor DSP,distribution scheduling under multi-task in the ARM-Linux,DSP handling procedures for multi-channel A / D high-speed sample. At the same time,because the traditional Gaussian fitting to determine the boundary of peak is not ideal,it puts forward a weighting factor of Gaussian function least squares fitting realize boundary determined. Finally gamma-spectrum data from sodium iodide NaI( TI) scintillation detector is tested and processed in the new system. The results show that gamma energy spectrum data acquisition and process system is perfect functionality, stable and convergence in unimodal. Compared with data from conventional energy spectrometers,the system can keep better energy resolution in a wide range of pulse pass rate.

Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

Signal processing circuit of laser gyro based on FPGA and DSP

This is a paper about laser gyro sign a l processing circuit which is designed based on field-programmable gate array(FPGA) and digital signal processor(DSP).Through a pre-amplifier circuit,FPGA and DSP,a weak current signal is converted and transferred,then sent to the computer to display the final results.Through the laser gyro performance te sting,the obtained results coincide with those of the existing methods.Thus th e d esigned circuit realizes the function of laser gyro signal processing.

Underground vibration signal detection and processing system based on LabWindows/CVI

In order to detect and process underground vibration signal, this paper presents a system with the combination of software and hardware. The hardware part consists of sensor, memory chips, USB, etc. , which is responsible for capturing original signals from sensors. The software part is a virtual oscilloscope based on LabWindows/CVI （C vitual instrument）, which not only has the functions of traditional oscilloscope but also can analyze and process vibration signals in special ways. The experimental results show that the designed system is stable, reliable and easy to be operated, which can meet practical requirements.

A New Signal Processing Technique of π/4-DQPSK Modem Based on Software Radio

DQPSK modem has been chosen as the modem scheme in many mobile communication systems. A new signal processing technique of π/4-DQPSK modem based on software radio is discussed in this paper. Unlike many other software radio solutions to the subject, we choose a universal digital radio baseband processor operating as the co-processor of DSP. Only the core algorithms for signal processing are implemented with DSP. Thus the computation burden on DSP is reduced significantly. Compared with the traditional ones, the technique mentioned in this paper is more promising and attractive. It is extremely compact and power-efficient, which is often required by a mobile communication system. The implementation of baseband signal processing for π/4-DQPSK modem on this platform is illustrated in detail. Special emphases are laid on the architecture of the system and the algorithms used in the baseband signal processing. Finally, some experimental results are presented and the performances of the signal processing and compensation algorithms are evaluated through computer simulations.

Kravchenko atomic transforms in digital signal processing

The modified atomic transformations are constructed and proved. On their basis the new complex analytic wavelets are obtained. The proof of the Fourier transforms existence in L~ and L2 on the basis of the theory of atomic functions （AF） are presented. The numerical experiments of digital time series processing and physical analysis of the results confirm the efficiency of the proposed transforms.

New WA-system of kravchenko functions in digital signal processing

On the basis of modified atomic transformations the new WA-systems of Kravchenko functions are constructed.As an example the digital processing of time series of the various physical nature processing is considered.The numerical experiments and physical analysis of the results confirm the efficiency of the proposed WA-systems of Kravchenko functions.

Training of Multi-layered Neural Network for Data Enlargement Processing Using an Activity Function

In this paper, we present a study on activity functions for an MLNN (multi-layered neural network) and propose a suitable activity function for data enlargement processing. We have carefully studied the training performance of Sigmoid, ReLu, Leaky-ReLu and L & exp. activity functions for few inputs to multiple output training patterns. Our MLNNs model has L hidden layers with two or three inputs to four or six outputs data variations by BP (backpropagation) NN (neural network) training. We focused on the multi teacher training signals to investigate and evaluate the training performance in MLNNs to select the best and good activity function for data enlargement and hence could be applicable for image and signal processing (synaptic divergence) along with the proposed methods with convolution networks. We specifically used four activity functions from which we found out that L & exp. activity function can suite DENN (data enlargement neural network) training since it could give the highest percentage training abilities compared to the other activity functions of Sigmoid, ReLu and Leaky-ReLu during simulation and training of data in the network. And finally, we recommend L & exp. function to be good for MLNNs and may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple teacher training patterns using original generated data and hence can be tried with CNN (convolution neural networks) of image processing.

A new data acquisition and processing system for profiling sonar

A multi-beam chirp sonar based on IP connections and DSP processing nodes was proposed and designed to provide an expandable system with high-speed processing and mass-storage of real-time signals for multi-beam profiling sonar.The system was designed for seabed petroleum pipeline detection and orientation,and can receive echo signals and process the data in real time,refreshing the display 10 times per second.Every node of the chirp sonar connects with data processing nodes through TCP/IP. Merely by adding nodes,the system’s processing ability can be increased proportionately without changing the software.System debugging and experimental testing proved the system to be practical and stable.This design provides a new method for high speed active sonar.

Real-Time Discrete Adaptive Control of Robot Arm Based on Digital Signal Processing

A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.

A Versatile High-speed Image Processing System Based on DSP and CPLD

In this paper, we present an optimized design method for high-speed embedded image processing system using 32 bit floating-point Digital Signal Processor （DSP） and Complex Programmable Logic Device （CPLD）. The DSP acts as the main processor of the system： executes digital image processing algorithms and operates other devices such as image sensor and CPLD. The CPLD is used to acquire images and achieve complex logic control of the whole system. Some key technologies are introduced to enhance the performance of our system. In particular, the use of DSP/BIOS tool to develop DSP applications makes our program run much more efficiently. As a result, this system can provide an excellent computing platform not only for executing complex image processing algorithms, but also for other digital signal processing or multi-channel data collection by choosing different sensors or Analog-to-Digital （A/D） converters.

Data Analysis Methods and Signal Processing Techniques in Gravitational Wave Detection

Gravitational wave detection is one of the most cutting-edge research areas in modern physics, with its success relying on advanced data analysis and signal processing techniques. This study provides a comprehensive review of data analysis methods and signal processing techniques in gravitational wave detection. The research begins by introducing the characteristics of gravitational wave signals and the challenges faced in their detection, such as extremely low signal-to-noise ratios and complex noise backgrounds. It then systematically analyzes the application of time-frequency analysis methods in extracting transient gravitational wave signals, including wavelet transforms and Hilbert-Huang transforms. The study focuses on discussing the crucial role of matched filtering techniques in improving signal detection sensitivity and explores strategies for template bank optimization. Additionally, the research evaluates the potential of machine learning algorithms, especially deep learning networks, in rapidly identifying and classifying gravitational wave events. The study also analyzes the application of Bayesian inference methods in parameter estimation and model selection, as well as their advantages in handling uncertainties. However, the research also points out the challenges faced by current technologies, such as dealing with non-Gaussian noise and improving computational efficiency. To address these issues, the study proposes a hybrid analysis framework combining physical models and data-driven methods. Finally, the research looks ahead to the potential applications of quantum computing in future gravitational wave data analysis. This study provides a comprehensive theoretical foundation for the optimization and innovation of gravitational wave data analysis methods, contributing to the advancement of gravitational wave astronomy.

面向HKSEDSPC6713系列DSP的主动噪声控制系统设计

微处理器芯片的生态建设是高端装备与智能微系统自主、可控的关键,尽管国产数字信号处理(digital signal processing, DSP)器件及其相关开发应用技术近年来得到了一定的发展,但与需求仍存在较大差距。在主动噪声控制领域,前馈型多通道控制方案比单通道有较大的控制范围和较好的性能,但对系统的运算能力有较高的要求。文章以多通道FxLMS算法为基础,对多通道降噪系统的运算量进行了分析,依据国产DSP开发板的电路结构,设计了控制系统方案,并进行了实验研究。实验表明,所设计的噪声控制系统运算效率较ARM作为运算器提高了80%,对100~1 000 Hz内的周期性噪声信号衰减达到15~20 dB,证明了该方案的正确性。

基于DSP的信号处理系统设计及应用

为了适应光电图像实时处理速度要求,设计了基于多核数字信号处理(digital signal processor,DSP)和现场可编程门阵列(field programmable gate array,FPGA)的信号处理系统。阐述了该系统的硬件及软件架构设计,图像采用光纤进行传输,可用于处理不同光波段的电荷耦合器件(charge coupled device,CCD)图像数据。以基于可见光CCD传感器的火灾监测系统为例,利用物体燃烧时火焰及烟雾等特征,采用图像特征提取算法对实时采集的图像进行处理,识别出火灾区域后进行上报。实验结果表明,该信号处理系统工作可靠稳定,具有图像高速并行处理能力,能够同时处理多路CCD图像,实现软件在线加载、图像存储卸载等功能,为光电图像处理系统平台提供一种通用解决方案。

基于DSP算法的广播电视发射基站信号实时信道均衡系统设计

设计一种应用于广播电视发射基站的信号实时信道均衡系统,该系统基于数字信号处理(Digital Signal Processing,DSP)算法,旨在提高信号传输质量并降低误码率。系统由信号预处理、信道估计、自适应均衡3个关键模块组成。信号预处理模块采用自适应滤波技术去除噪声和干扰;信道估计模块利用频域分析技术精确估计信道参数;自适应均衡模块则通过最小均方误差(Least Mean Square,LMS)算法动态调整均衡器系数,以补偿信道失真。实验结果表明,该系统在城市、郊区、山区环境下均能显著提高信号质量,降低误码率,并提供足够的信道容量,满足广播电视信号的高质量实时传输需求。

试论基于DSP的信号处理系统在电子信息工程中的应用

本文进一步分析了电子信息工程与信号处理系统之间的关联,并对电子信息工程中基于DSP的信号处理系统应用优势加以阐述。从应用设计思路、系统架构及应用实践等方面探究基于DSP的信号处理系统在电子信息工程中的具体应用,以期充分发挥DSP信号处理系统的优势,提高数据处理效率,优化系统综合性能,充分满足电子信息工程综合实践需要。

DSP在线无缝升级方法研究

数字信号处理(DSP)在线升级需要多次断电重启,面对突发情况可能造成升级失败无法回退原版本的情况,为此对DSP在线升级方法进行了优化。通过加入外部看门狗实现整个升级过程无需断电重启。在FLASH分区中引入了AB区方式,实现DSP在线无缝升级并提高了在线升级的容错性。通过实验充分验证了上述优化方法,证明了该在线无缝升级方法的易操作性和容错性,可广泛应用于嵌入式系统在线升级过程。

基于DSP模式的计算机图像处理算法研究

基于DSP架构模式,提出了一种CNN卷积神经网络算法,并将其运用到计算机图像处理中。研究过程中,采用DSP技术进行计算机图像获取、算法处理、算例分析和结果优化,大大提高了计算机图像算法处理质量和效率。经过算法测试验证,结果表明,基于DSP数字信号处理器搭建多DSP并行处理架构模式,采用CNN卷积神经网络算法进行计算机图像处理,能够提高图像处理精度。该算法运行时的性能较高,功能低,CPU占用率不高,且DSP计算机处理系统在多DSP并行处理架构模式下进行算法分析,系统的稳健性和可靠性高,能够适应不同规模级别下的计算机图像处理数据集的处理速度、精度、资源消耗和功率要求,可为计算机图像算法处理提供准确、高效、经济的解决方案,对于计算机图像处理算法设计和优化以及应用具有较好的实用参考价值。

智能传感技术在水肥一体系统中的应用研究

以进一步提升水肥一体机系统的作业效率为目标,选取智能传感的监测技术,针对整机的监测控制与信号处理展开应用设计研究。考虑水肥一体机过程作业肥液融合的均匀性及系统各模块之间的协同性功能实现,结合微分补偿的传感数据算法处理方法,进行智能传感的水肥一体机架构布局,并匹配可执行的软件控制程序及硬件实施结构,进行实地传感应用监测与灌施控制作业试验。结果表明:水肥一体机系统的数据监测准确率可达95.25%,系统故障率相对降低3.79%,监测数据准确及时,能够确保系统各环节指令得到有效的调整与反馈,进而保证灌施土壤的含水稳定率相对提升7.87%,对于作物的稳定生长与产量提升有重要的参考价值。

DSP技术在线阵CCD测量系统中的应用

本文介绍数字信号处理器DSP在线阵CCD信号实时采集与处理系统中的应用。测量系统采用CCD传感器光采样与ADC数据采集、DSP数据处理三级流水线结构。结合AD采样技术 ,充分发挥DSP处理器片内存储器容量大、快速灵活运算能力的特点 ,使用DSP片内双数据缓冲区交替对CCD进行信号采集和数据处理 ;在进行数据处理过程中 ,DSP以中断方式读取AD采样结果。