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Journal articles on the topic 'Digital signal processing system'

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Published: 10 December 2022

Last updated: 29 January 2023

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1

Zhuang, Qiu Hui, Guo Jun Liu, Xiu Hua Fu, and San Qiang Wang. "Brain Electrical Signal Digital Processing System Design." Applied Mechanics and Materials 278-280 (January 2013): 958–61. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.958.

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Through the amplification system to extract the brain electrical signal, although already can be displayed, but is not clear; in addition, the analog signal into the computer to carry on the analysis, also must pass to convert analog signals to digital signals (A/D converter).Therefore the need for further use of digital processing, this paper adopts the digital way, on brain electrical analog signal digital filter, through the 40Hz low-pass filter and 50Hz filter, get clear, stable signal, to achieve the design objective.

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2

Yamaguchi, Hirohisa, and Yoshito Higa. "Digital signal processing acoustic speaker system." Journal of the Acoustical Society of America 116, no. 3 (2004): 1320. http://dx.doi.org/10.1121/1.1809884.

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3

Hung, Phong, and Vu Duc Vuong. "Analog Signal and Digital Signal Processing in Telecommunication System." Journal La Multiapp 1, no. 6 (January 14, 2021): 1–5. http://dx.doi.org/10.37899/journallamultiapp.v1i6.277.

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The term digital signal is a term from a technology that converts an analog signal into digital data so that the signal can be processed more easily and quickly. The term digital itself is a system that only recognizes two conditions. The two conditions are usually represented by the numbers zero and one, on and off, or others. The smallest unit of digital signal is the bit.

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4

Zhao, Changhai, Qiuhua Wan, Lihui Liang, and Ying Sun. "Full Digital Processing System of Photoelectric Encoder." Sensors 19, no. 22 (November 9, 2019): 4892. http://dx.doi.org/10.3390/s19224892.

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A photoelectric signal, output by a photoelectric receiver, may detrimentally change after the photoelectric encoder is used for a period of time or when the environment changes; this will directly affect the accuracy of the encoder and lead to fatal errors in the encoder. To maintain its high accuracy, we propose an encoder that can work in a variety of environments and that adopts full digital processing. A signal current that travels from the receiver of a photoelectric encoder is converted into a voltage signal via current limiting resistance. All signals are directly processed in the data processor component of the system. The encoder converts all the signals into its normalized counterpart. Then, the angle of the encoder is calculated using the normalized value. The calculated encoder angle compensates for any error. The final encoder angle is obtained, and the encoder angle is output accordingly. Experiments show that this method can greatly reduce the encoder’s volume. This method also reduces the encoder error from 167 arcseconds to 53 arcseconds. The encoder can still maintain a high accuracy during environmental changes, especially in harsh environments where there are higher accuracy requirements.

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5

Steffen, Peter. "Digital signal processing." Signal Processing 21, no. 4 (December 1990): 355–56. http://dx.doi.org/10.1016/0165-1684(90)90106-9.

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6

Ohtsubo, Hiroyasu. "Digital Signal Processing System for Video Camera." Journal of the Institute of Television Engineers of Japan 45, no. 9 (1991): 1060–66. http://dx.doi.org/10.3169/itej1978.45.1060.

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7

Xu, Ke-Jun, Zhi-Hai Zhu, Yang Zhou, Xiao-Fen Wang, San-Shan Liu, Yun-Zhi Huang, and Zhi-Yuan Chen. "Applied digital signal processing systems for vortex flowmeter with digital signal processing." Review of Scientific Instruments 80, no. 2 (February 2009): 025104. http://dx.doi.org/10.1063/1.3082044.

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8

Saeed, Amer T., Zaid Raad Saber, Ahmed M. Sana, and Musa A. Hameed. "Eliminating unwanted signals in sound by using digital signal processing system." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 829. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp829-834.

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<p><a name="_Hlk536186602"></a><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Unwanted signals or noise signals in sound files are considered one of the major challenges and issues for a thousand users. It is impossible to reduce or remove these noise signals without identifying their types and ranges. Therefore, to address one of the big problems in the digital or analogue communication, which is noise signals or unwanted signals, an adaptive selection method and noise signal removal algorithm are proposed in this research. The proposed algorithm is done through specifying the types of undesirable signals, frequency, and time range, then utilizing digital signal processing system which includes design several types of digital filters based on the types and numbers of unwanted signals. Four digital filters are used in this research to remove noise signals from the sound file by implementing the proposed algorithm using Matlab Code. Results show that our proposed algorithm was done successfully and the whole noise signals were removed without any negative consequence in the output sound signal. </span><span style="font-family: 'Times New Roman', serif; font-size: 9pt;">Unwanted signals or noise signals in sound files are considered one of the major challenges and issues for a thousand users. It is impossible to reduce or remove these noise signals without identifying their types and ranges. Therefore, to address one of the big problems in the digital or analogue communication, which is noise signals or unwanted signals, an adaptive selection method and noise signal removal algorithm are proposed in this research. The proposed algorithm is done through specifying the types of undesirable signals, frequency, and time range, then utilizing digital signal processing system which includes design several types of digital filters based on the types and numbers of unwanted signals. Four digital filters are used in this research to remove noise signals from the sound file by implementing the proposed algorithm using Matlab Code. Results show that our proposed algorithm was done successfully and the whole noise signals were removed without any negative consequence in the output sound signal.</span></p>

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9

Takala, Jarmo, Shuvra S. Bhattacharyya, and Gang Qu. "Embedded Digital Signal Processing Systems." EURASIP Journal on Embedded Systems 2007 (2007): 1. http://dx.doi.org/10.1155/2007/27517.

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10

Takala, Jarmo, ShuvraS Bhattacharyya, and Gang Qu. "Embedded Digital Signal Processing Systems." EURASIP Journal on Embedded Systems 2007, no. 1 (2007): 027517. http://dx.doi.org/10.1186/1687-3963-2007-027517.

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11

Ching, PC, and SW Wu. "Realtime digital signal processing system using a parallel processing architecture." Microprocessors and Microsystems 13, no. 10 (December 1989): 653–58. http://dx.doi.org/10.1016/0141-9331(89)90073-2.

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12

Chen, Qunying. "Stepped Frequency Multiresolution Digital Signal Processing." Scientific Programming 2021 (June 8, 2021): 1–13. http://dx.doi.org/10.1155/2021/9081988.

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With the rapid development of radar industry technology, the corresponding signal processing technology becomes more and more complex. For the radar with short-range detection function, its corresponding signal mostly presents the characteristics of wide bandwidth and multiresolution. In the traditional data processing process, a large number of signals will interfere with the signal, which makes the final signal processing difficult or even impossible. Based on this problem, this paper proposes a principal component linear prediction processing algorithm based on clutter suppression processing on the basis of traditional signal processing algorithm. According to the curve characteristics of the data returned by the target detected by the signal, through certain image signal measurement and transformation, the clutter can be effectively suppressed and the typical characteristics of the corresponding target curve can be enhanced. For the convergence problem of signal processing and the corresponding image chromatic aberration compensation problem, this paper will realize the chromatic aberration compensation of the corresponding target echo image based on the radial pointing transverse mode algorithm and enhance the convergence speed of the whole algorithm system. In the experimental part of this paper, the optimization algorithm proposed in this paper is compared with the traditional algorithm. The experimental results show that the algorithm proposed in this paper has obvious advantages in the convergence of signal processing and antijamming performance and has the promotion value.

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13

Valderrama-Cuervo, Juan Camilo, and Alexander López-Parrado. "OPEN CORES FOR DIGITAL SIGNAL PROCESSING." Revista de Investigaciones Universidad del Quindío 25, no. 1 (May 31, 2014): 53–62. http://dx.doi.org/10.33975/riuq.vol25n1.150.

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This paper presents the design and implementation of three System-on-Chip (SoC) cores, which implement the Digital Signal Processing (DSP) functions: Finite Impulse Response (FIR) filter, Infinite Impulse Response (IIR) filter and Fast Fourier Transform (FFT). The FIR-filter core is based on the symmetrical realization form, the IIRfilter core is based on the Second Order Sections (SOS) architecture and the FFT core is based on the Radix 22 Single Delay Feedback (R22SDF) architecture. The three cores are compatible with the Wishbone SoC bus, and they were described using generic and structural VHDL. In-system hardware verification was performed by using an OpenRisc-based SoC synthesized on an Altera FPGA. Tests showed that the designed DSP cores are suitable for building SoC based on the OpenRisc processor and the Wishbone bus.

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14

Marciniak, T., R. Weychan, A. Stankiewicz, and A. Dąbrowski. "Biometric speech signal processing in a system with digital signal processor." Bulletin of the Polish Academy of Sciences Technical Sciences 62, no. 3 (September 1, 2014): 589–94. http://dx.doi.org/10.2478/bpasts-2014-0064.

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Abstract This paper presents an analysis of issues related to the fixed-point implementation of a speech signal applied to biometric purposes. For preparing the system for automatic speaker identification and for experimental tests we have used the Matlab computing environment and the development software for Texas Instruments digital signal processors, namely the Code Composer Studio (CCS). The tested speech signals have been processed with the TMS320C5515 processor. The paper examines limitations associated with operation of the realized embedded system, demonstrates advantages and disadvantages of the technique of automatic software conversion from Matlab to the CCS and shows the impact of the fixed-point representation on the speech identification effectiveness.

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15

Braccini, C. "Digital image signal processing." Signal Processing 17, no. 2 (June 1989): 185–86. http://dx.doi.org/10.1016/0165-1684(89)90023-6.

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16

Zeng, Xiao Teng. "Signal Acquisition and Processing in Hydraulic Components Test System." Advanced Materials Research 846-847 (November 2013): 972–76. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.972.

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The key to the performance test system for hydraulic components is to realize real, fast and accurate signal acquisition and processing. By analysis the analog and digital signal processing during hydraulic components Performance testing, a new signal flow scheme is proposed. It described in detail the acquisition and processing method of serial signals and PLC signals, thus solving the lag and distortion during signal processing and realizing the real-time display of data and the automatic report of performance parameters during hydraulic components testing process.

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17

McCollough, W. Vance, Diane M. Knight, David Erickson, and Raymond A. Jannsen. "A real‐time digital signal processing evaluation system." Journal of the Acoustical Society of America 78, S1 (November 1985): S79. http://dx.doi.org/10.1121/1.2023002.

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18

Hatanaka, K., Y. Shirasaki, N. Fujiwara, N. Watanabe, T. Furukawa, and S. Kawabe. "A digital FM signal processing system for VCRs." IEEE Transactions on Consumer Electronics 41, no. 3 (1995): 405–13. http://dx.doi.org/10.1109/30.468053.

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19

Hulskamp, John P. "Continuous system simulation using digital signal processing elements." Mathematics and Computers in Simulation 27, no. 2-3 (April 1985): 129–36. http://dx.doi.org/10.1016/0378-4754(85)90031-x.

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20

Khudorozhkov, O. V., S. A. Silvashko, and V. N. Bulatov. "Method of Digital Signal Reproduction for Digital Processing System Certification of Radio Interference Signals." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 24, no. 2 (2018): 190–202. http://dx.doi.org/10.17277/vestnik.2018.02.pp.190-202.

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21

Chen, Jian Xiang, and Hong Jun Sun. "The Digital Signal Processing Algorithm Implemented on ARM Embedded System." Advanced Materials Research 756-759 (September 2013): 3958–61. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3958.

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Digital Signal Processing technology is an important tool for modern signal processing. The speedy development ARM embedded processor has powerful computed capability for digital signal processing algorithms. The paper provides a common hardware platform in recent years. In this paper, used assemble language to realize the algorithms and FIR Filter based on ARM-Linux embedded environment. The results shown that the ARM can quickly and efficiently complete a series of digital signal processing algorithms. Digital signal processing algorithm on ARM embedded system provides an effective way.

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22

Gao, Feng, Yun Wu, and Shang Qiong Lu. "LabVIEW-Based Virtual Laboratory for Digital Signal Processing." Advanced Materials Research 268-270 (July 2011): 2150–57. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.2150.

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Based on National Instruments LabVIEW 2009, a network-edition virtual laboratory for digital signal processing (DSP) has been developed. Which is composed of three functional modules, that is, virtual experiment table, information management, and network communication. Hereinto, virtual experiment table is composed of two sub-modules, i.e. resource & document and simulation experiment; information management module is composed of four sub-modules, i.e. database, user registration, security verification and system management; network communication module is implemented by LabVIEW Web Server. The DSP Virtual Laboratory is suit for experimental teaching of a range of subjects, such as Digital Signal Processing, Signals & Systems, etc. And the designed virtual laboratory can provide users with a remote virtual experimental platform without time and space constraints.

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23

Chen, Aiyong. "Digital Filtering Technology in Industrial Measuring and Control System." Electronics Science Technology and Application 2 (December 3, 2015): 42. http://dx.doi.org/10.18686/esta.v2i1.8.

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<p>This article aims at the technical problems in modernized industrial measuring and control system such as interruption signal, noise signal and other useless signal. First introduces the features and importance of digital filtering technology and then elaborates on the realization methods of digital filtering and frequently used digital filtering calculation methods in the industrial measuring system. The research reveals that integrated usage of numerous methods or even complex digital filtering technology is adopted to calculate and treat such digital signals like random interruption, heat noise, system noise, measuring error and zero-point offset and thus meet the system requirements. The digital filtering technology is widely applied in the processing of HD signal, such as digital audio, radar, image processing, data transmission and biological and medical fields and pledges to provide strong assurance for the real-time, stable and reliable properties of modernized industrial measuring and control system.</p><div> </div><div> </div>

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24

Daly, Daniel F. "Digital signal processing circuit board having use for voice processing system." Journal of the Acoustical Society of America 99, no. 6 (1996): 3284. http://dx.doi.org/10.1121/1.414940.

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25

Rawski, Mariusz, Bogdan Falkowski, and Tadeusz Łuba. "Digital signal processing designing for FPGA architectures." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 437–59. http://dx.doi.org/10.2298/fuee0703437r.

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This paper presents the discussion on efficiency of different implementation methodologies of DSP algorithms targeted for modern FPGA architectures. Modern programmable structures are equipped with specialized DSP embedded blocks that allow implementing digital signal processing algorithms with use of the methodology known from digital signal processors. On the first place however, programmable architectures give the designer the possibility to increase efficiency of designed system by exploitation of parallelism of implemented algorithms. Moreover, it is possible to apply special techniques such as distributed arithmetic (DA) that will boost the performance of designed processing systems. Additionally, application of the functional decomposition based methods, known to be best suited for FPGA structures allows utilizing possibilities of programmable technology in very high degree. The paper presents results of comparison of different design approaches in this area.

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26

Young, T., and H. Saunders. "Linear Systems and Digital Signal Processing." Journal of Vibration and Acoustics 110, no. 4 (October 1, 1988): 574–75. http://dx.doi.org/10.1115/1.3269573.

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27

de Coulon, Frédéric. "Linear systems and digital signal processing." Signal Processing 12, no. 2 (March 1987): 219. http://dx.doi.org/10.1016/0165-1684(87)90011-9.

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Zhang, Jing Bo, Xiao Feng Wang, and Shu Fang Zhang. "Audio Signal Processing Based on FPGA." Advanced Materials Research 1049-1050 (October 2014): 1759–64. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1759.

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This paper presents a system of audio signal processing based on FPGA,the system uses audio codec chip LM4550 to A/D transform and D/A transform the input analog audio signal and output digital audio signal.Using FPGA as the high speed signal processor to realize volume adjustment and audio effect control,so it can output different style music.Meantime, the system designs a FFT computing module and control system of VGA display interface,to compute the digital audio signal which is A/D transformed,and real-time display the frequency spectrum of audio signal on VGA.

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29

Javidi, Giti, and Ehsan Sheybani. "Application of Digital Signal Processing in USRP Satellite Signal Detection." International Journal of Interdisciplinary Telecommunications and Networking 9, no. 2 (April 2017): 16–25. http://dx.doi.org/10.4018/ijitn.2017040102.

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The Universal Software Radio Peripheral development technique is designing and implementing radio frequency based systems. The distinctiveness originates from the interchangeable daughterboard within the USRP. The system is designed around the Xilinx Vertex 3 FPGA chip. This means C++, Python, and VHDL can be used to program this device. The project consists of creating a receiver. The objective of the project is to research and comprehend the hardware functionalities of the USRP. The purpose is to create codes in C++ and Python to implement receiving capabilities of the device. The goal of this project was to design a GPS receiver that is capable of recording the L1 signal from a DirecTV satellite. The USRP is used a lot for research. This project consisted of more than just one method. We used GNU Radio Companion and Matlab/Simulink. GNU Radio is open source for building software defined radios. It is also known as GRC. While using GRC the USRP1 was the device used. This software has rapid development. It runs in Ubuntu, a Linux operating system. Within this software there are logic blocks. Each block consists of information to create a flow graph. The flow graph builds and generates the program. Simulink can be compared to GRC. They both have logic blocks that have to be connected to run. Simulink can be used to create a transmitter or a receiver for software radio development and signal processing. Software-defined radio can only be defined if its baseband operations can be completely defined by software. A SDR converts digital to analog signals. The USRP can also convert digital signals from a computer to Radio Frequency Signals (RF). This software is one way to communicate between hardware and software.

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30

Liu, Shi Wei, and Shi Bin Liu. "Design and Realization of a Digital Multichannel Fluxgate Signal Processing System." Applied Mechanics and Materials 182-183 (June 2012): 491–95. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.491.

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Addressing drawbacks of analog components and questions of multi-channel fluxgate signal operation, a FPGA (Field Programmable Gate Array) based signal processing system is designed. Three copies of sub modules compose the whole system, each of which exclusively processes one of three outputs of the fluxgate sensor. A “Phase-Sensitive-Rectification & Low-Pass-Filtering” circuit structure is employed in the processing module, through which the fluxgate signal harmonics are extracted and converted into direct quantities according to detected magnetic intensities. Firstly designed in HDL (Hardware Description Language), afterward configurated in a FPGA chip, finally tested by processing outputs of a fluxgate sensor probe in real-time, the functionality of the designed system is verified. With inherent advantages, this FPGA based design is much reliable over temperature; by processing signals not time-sharingly but synchronously, its working speed is excellently high.

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31

Xin, Qin, Zhihong Jiang, Pu Cheng, and Mi He. "Signal Processing for Digital Beamforming FMCW SAR." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/859890.

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According to the limitations of single channel Frequency Modulation Continuous Wave (FMCW) Synthetic Aperture Radar (SAR), Digital Beamforming (DBF) technology is introduced to improve system performance. Combined with multiple receive apertures, DBF FMCW SAR can obtain high resolution in low pulse repetition frequency, which can increase the processing gain and decrease the sampling frequency. The received signal model of DBF FMCW SAR is derived. The continuous antenna motion which is the main characteristic of FMCW SAR received signal is taken into account in the whole signal processing. The detailed imaging diagram of DBF FMCW SAR is given. A reference system is also demonstrated in the paper by comparing with a single channel FMCW SAR. The validity of the presented diagram is demonstrated with a point target simulation results.

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32

Deng, Shu Zhang. "Research on DSP Embedded Digital Signal Processing System for Ship Navigation Radar." Applied Mechanics and Materials 556-562 (May 2014): 4718–21. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4718.

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Development of digital signal processing and embedded technologies today, to the development of radar technology has brought new opportunities. Relative to the simulation of radar, digital radar has good performance, features, ease of operation, and other benefits. Design based on embedded processor and digital signal processor (DSP) dual processor shipboard navigation radar system, and focuses on digital shipborne navigational radar system architecture, hardware design and software algorithms for digital signal processing module, gives the overall scheme for DSP systems.

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33

Piskur, Pawel, and Marek Gasiorowski. "Digital Signal Processing for Hydroacoustic System in Biomimetic Underwater Vehicle." Naše more 67, no. 1 (March 2020): 14–18. http://dx.doi.org/10.17818/nm/2020/1.3.

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34

Mellinger, David K., G. E. Garnett, and Bernard Mont-Reynaud. "Virtual Digital Signal Processing in an Object-Oriented System." Computer Music Journal 13, no. 2 (1989): 71. http://dx.doi.org/10.2307/3680042.

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35

Ghassemlooy, Z. "Book Review: Digital Signal Processing. System Analysis and Design." International Journal of Electrical Engineering & Education 43, no. 3 (July 2006): 275. http://dx.doi.org/10.7227/ijeee.43.3.9.

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36

Skavantzos, A., and F. J. Taylor. "On the polynomial residue number system (digital signal processing)." IEEE Transactions on Signal Processing 39, no. 2 (1991): 376–82. http://dx.doi.org/10.1109/78.80821.

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37

Uomori, K., A. Morimura, H. Ishii, T. Sakaguchi, and Y. Kitamura. "Automatic image stabilizing system by full-digital signal processing." IEEE Transactions on Consumer Electronics 36, no. 3 (1990): 510–19. http://dx.doi.org/10.1109/30.103167.

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38

Schuck, J., N. Wehn, and M. Glesner. "ALGIC—a silicon compiler system for digital signal processing." Computer-Aided Engineering Journal 5, no. 5 (1988): 191. http://dx.doi.org/10.1049/cae.1988.0043.

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39

Mercs, Laura, and Paul M. Embree. "Audio noise reduction system implemented through digital signal processing." Journal of the Acoustical Society of America 108, no. 2 (2000): 474. http://dx.doi.org/10.1121/1.429557.

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40

Schuck, J., N. Wehn, and M. Glesner. "ALGIC — a silicon compiler system for digital signal processing." Computer-Aided Design 21, no. 3 (April 1989): 184. http://dx.doi.org/10.1016/0010-4485(89)90080-8.

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41

Jaski, Petri K. "Handbook for digital signal processing." Signal Processing 35, no. 1 (January 1994): 98–99. http://dx.doi.org/10.1016/0165-1684(94)90198-8.

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42

Darlis, Arsyad Ramadhan. "Fractal Communication System Using Digital Signal Processing Starter Kit (DSK) TMS320c6713." ComTech: Computer, Mathematics and Engineering Applications 6, no. 4 (December 1, 2015): 613. http://dx.doi.org/10.21512/comtech.v6i4.2202.

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In 1992, Wornell and Oppenheim did research on a modulation which is formed by using wavelet theory. In some other studies, proved that this modulation can survive on a few channels and has reliability in some applications. Because of this modulation using the concept of fractal, then it is called as fractalmodulation. Fractal modulation is formed by inserting information signal into fractal signals that are selffractal similary. This modulation technique has the potential to replace the OFDM (Orthogonal Frequency Division Multiplexing), which is currently used on some of the latest telecommunication technologies. The purpose of this research is to implement the fractal communication system using Digital Signal Processing Starter Kit (DSK) TMS320C6713 without using AWGN and Rayleigh channel in order to obtain the ideal performance of the system. From the simulation results using MATLAB7.4. it appears that this communication system has good performance on some channels than any other communication systems. While in terms of implementation by using (DSK) via TMS320C6713 Code Composer Studio (CCS), it can be concluded that thefractal communication system has a better execution time on some tests.

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43

YAN, LIUMING, YUEFEI MA, and JORGE M. SEMINARIO. "TERAHERTZ SIGNAL TRANSMISSION IN MOLECULAR SYSTEMS." International Journal of High Speed Electronics and Systems 16, no. 02 (June 2006): 669–75. http://dx.doi.org/10.1142/s0129156406003928.

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Terahertz signal transmission in DNA is simulated and analyzed using molecular dynamics and digital signal processing techniques to demonstrate that signals encoded in vibrational movements of hydrogen bonds can travel along the backbone of DNA and eventually be recovered and analyzed using digital signal processing techniques.

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44

Nasrulloh, Mohammad Dicky. "Designing a Digital Filter Based Crossover Audio System Using STM32L4." Jurnal Jartel: Jurnal Jaringan Telekomunikasi 9, no. 4 (December 25, 2019): 13–18. http://dx.doi.org/10.33795/jartel.v9i4.141.

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Analog telecommunication system equipment is now starting to develop and be replaced with digital telecommunication systems, one of them is in the audio signal processing. The focus of audio processing is audio crossover. Audio crossover in development there are still many who use analog systems. This analog system has disadvantages when adjusting the sound balance because it still uses analog filters to balance it. It is necessary to develop a technology that aims to create a digital-based crossover audio system using the STM32L4, so that by using this digital-based signal processing it is able to adjust the sound more specifically than the signal processing used analog based. This digital filter uses the Finite Impulse Response (FIR) method. Testing audio crossover using STM32L4 produces a digital-based crossover audio system design using a STM32L4 microcontroller with a voltage of 3.3V as power supply, mp3 player as sound input device, FIR filter as digital filter processing, LM386 as sound amplifier and speaker as sound output for crossover audio on rangelow frequency (200Hz to 4000Hz), high (2200Hz to 6000Hz), medium (200Hz to 4000Hz).

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45

Herman, Krzysztof, Tadeusz Gudra, and Joanna Furmankiewicz. "Digital Signal Processing Approach in Air Coupled Ultrasound Time Domain Beamforming." Archives of Acoustics 39, no. 1 (March 1, 2015): 37–50. http://dx.doi.org/10.2478/aoa-2014-0005.

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Abstract The work presents the results of experimental study on the possibilities of determining the source of an ultrasonic signal in two-dimensional space (distance, horizontal angle). During the research the team used a self-constructed linear array of MEMS microphones. Knowledge in the field of sonar systems was utilized to analyse and design a location system based on a microphone array. Using the above mentioned transducers and broadband ultrasound sources allows a quantitative comparison of estimation of the location of an ultrasonic wave source with the use of broadband modulated signals (modelled on bats' echolocation signals) to be performed. During the laboratory research the team used various signal processing algorithms, which made it possible to select an optimal processing strategy, where the sending signal is known.

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Talanov, M. V., and V. M. Talanov. "Software and hardware solution for digital signal processing algorithms testing." E3S Web of Conferences 124 (2019): 03006. http://dx.doi.org/10.1051/e3sconf/201912403006.

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The article describes the microprocessor system for various digital signal processing algorithms testing. The development of electric drive control systems is carried out with the usage of modeling systems such as, MATLAB/Simulink. Modern digital control systems are based on specialized digital signal microcontrollers. The present market offers evaluation boards, for example STM32F4DISCOVERY, which enables to connect a microcontroller to a personal computer. It makes possible to use the microcontroller as a part of the mathematical model of the control system. However, the designing of the control system simulation model and the program for the microprocessor is carried out in different programming environments. Thus, the software and hardware solution for testing programs for the microprocessor, which is a part of the control system, is relevant. This article deals with the designing of the modeling method in which the prototype program for the microprocessor is debugged as a part of the electric drive control system simulation model.

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Osten, Evariste F., and John C. Schultz. "A system for fast digital image processing of asynchronous SEM signals." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 676–77. http://dx.doi.org/10.1017/s0424820100105448.

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The time required to examine a specimen's features with an SEM before photographically recording representative images is related to the amount of visual information about that specimen that is available from the SEM's viewing CRT. In a laboratory that examines several thousand specimens each year, many in low signal-to-noise situations, the accumulated examination time can be significant. Image processing to increase the information content of the viewed image can reduce the time needed to examine the specimen. Digital frame integration can be used to improve an image's signal-to-noise ratio and color processing of the observed image can be used to provide enhanced visual perception. Using a passive interface with the SEM for image processing has the advantage that it doesn't interfere with the SEM scan electronics nor does it affect normal SEM operation. A difficulty in image processing arises when using asynchronous SEM signals - video signals that lack synch pulses and therefore do not conform to standard RS-170 video.

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Tojo, Keiichiro, Minoru Kurosawa, Koichi Oka, and Toshiro Higuchi. "Resolution of digital servo control system using single-bit digital signal processing." IEEJ Transactions on Industry Applications 118, no. 5 (1998): 623–29. http://dx.doi.org/10.1541/ieejias.118.623.

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Gaydecki, Patrick. "The Foundations of Digital Signal Processing Using Signal Wizard Systems®." International Journal of Electrical Engineering & Education 49, no. 3 (July 2012): 310–20. http://dx.doi.org/10.7227/ijeee.49.3.10.

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Signal Wizard Systems® is a digital signal processing (DSP) research venture within the School of EEE at the University of Manchester, UK. It specialises in the development and supply of real-time DSP products for audio signal analysis and processing. The unique and underpinning philosophy of these products is their ease of use. The systems require minimal knowledge of DSP theory on the part of the user and none of the mathematics associated with digital filter design. Filters and other algorithms can be designed in seconds, downloaded and executed in real time with just a few mouse clicks. Since 2004 Signal Wizard products have been sold all over the world for applications ranging from noise suppression, adaptive filtering and system modelling to musical instrument research. In particular, their ease of use ensures that they are ideally suited for teaching simple and more advanced concepts in DSP both at undergraduate and postgraduate level. For this purpose, a DSP laboratory teaching package has been developed using the Signal Wizard range of devices, and has proven an invaluable tool for training our student cohort in the practical aspects of DSP engineering design and programming.

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Yang, Chongyi, Chanpin Chen, Zhenhao Wu, Jiashun Jiang, Sicheng Su, Xue Kang, and Qingping Dou. "Multi-Channel Digital Oscilloscope Implementation over Android Device." Computer and Information Science 12, no. 2 (March 25, 2019): 58. http://dx.doi.org/10.5539/cis.v12n2p58.

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Signal monitoring and waveform analysis play a significant role in state-of-the-art signal processing and electronic measurement. Traditional oscilloscopes tend to be heavy and huge, which makes it impossible for outdoor signal measurement. In addition, most of those oscilloscopes can measure merely two signals simultaneously. This article proposes the design of multi-channel digital oscilloscope over common Android mobile device. In our system we use STM32 development board to implement up to eight input channels, data processing and wireless transmission. In addition, an Android application is designed for Wi-Fi data reception, respective waveform demonstration and derivation of each signal’s amplitude and frequency. In order to transmit up to eight digital signals simultaneously as fast as possible, we designed an algorithm where all signals’ data can be transmitted within a surprisingly small amount of wireless data. In our system test, wireless data transmission is implemented and each waveform can be recovered and demonstrated basically.

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