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Journal articles on the topic 'Signal processing Digital techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

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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2

Smith, Steward G., Ralph W. Morgan, and Julian Payne. "ASIC techniques for high-performance digital signal processing." Annales des Télécommunications 46, no. 1-2 (January 1991): 40–48. http://dx.doi.org/10.1007/bf02995434.

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Mikkelsen, H. F. "Using digital signal processing techniques in light controllers." IEEE Transactions on Consumer Electronics 39, no. 2 (May 1993): 122–30. http://dx.doi.org/10.1109/30.214817.

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4

Denisov, V. S., and Yu A. Rogovsky. "Development of Digital Signal Processing Techniques with BPM." SIBERIAN JOURNAL OF PHYSICS 19, no. 2 (July 15, 2024): 23–32. http://dx.doi.org/10.25205/2541-9447-2024-19-2-23-32.

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In order to ensure the continuity of operation of the VEPP-2000 collider, accurate measurement of the betatron frequen- cy is necessary. To do this, this work proposes to use methods that refine the Fourier transform, such as parabola inter- polation (Gassior method), NAFF and window functions. The refined frequency is subsequently used in construction of phase portraits of the beam to control the interference of high-order magnetic fields. In addition, the work discusses methods for extracting a signal from a mixture for subsequent analysis – PCA and ICA. Finally, to improve the accuracy of frequency determination, the paper considers the simplest implementation of a Kalman filter to improve the accuracy of subsequent harmonic analysis. In addition to all of the above, the paper briefly discusses a method for monitoring the operation of the beam position monitors themselves.

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Raghavendra, V., N. Vinay kumar, and Manish Kumar. "Latest advancement in image processing techniques." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 390. http://dx.doi.org/10.14419/ijet.v7i2.12.11357.

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Image processing is method of performing some operations on an image, for enhancing the image or for getting some information from that image, or for some other applications is nothing but Image Processing [1]. Image processing is one sort of signal processing, where input is an image and output may be an image, characteristics of that image or some features that image [1]. Image will be taken as a two dimensional signal and signal processing techniques will be applied to that two dimensional image. Image processing is one of the growing technologies [1]. In many real time applications image processing is widely used. In the field of bio technology, computer science, in medical field, envi-ronmental areas etc., image processing is being used for mankind benefits. The following steps are the basics of image processing:Image is taken as an inputImage will be processed (manipulation, analyzing the image, or as per requirement)Altered image will be the outputImage processing is of two typesAnalog Image Processing:As the name implies, analog image processing is applied on analog signals. Television image is best example of analog signal processing [1].(DIP) Digital Image Processing:DIP techniques are used on images, which are in the format of digital for processing them, and get the required output as per the application. Operations were applied on the digital images for processing [1].In this paper, we will discuss about the technologies or tools for image processing especially by using Open CV. With the help of Open CV image processing will be very easy and efficient. When Open CV is collaborated or integrated with python the results are mind blowing. We will discuss about the process of using python and Open CV.

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Siddiqui, Mohd Maroof, Mohd Suhaib Kidwai, Geetika Srivastava, K. K. Singh, and Piyush Charan. "Analysis of Eeg Data Using Different Techniques of Digital Signal Processing." Biomedical and Pharmacology Journal 17, no. 1 (March 20, 2024): 135–39. http://dx.doi.org/10.13005/bpj/2841.

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This paper explores the application of digital signal processing (DSP) techniques in the examination of electroencephalogram (EEG) data. DSP encompasses a collection of mathematical algorithms designed to employ signals, such as EEG recordings, and finds application in diverse domains, including sleep medicine, neuroscience, and biomedical engineering. Employing DSP methods for EEG data analysis enables the extraction of pertinent insights from EEG signals, the identification of event-related patterns, and the enhancement of diagnostic and therapeutic practices across various disciplines. This article provides an overview of prevalent DSP methodologies employed in EEG signal processing, encompassing filtering, power spectral analysis, wavelet analysis, independent component analysis, and artifact removal.

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Laddomada, M., G. J. Dolecek, L. Yong Ching, Fa-Long Luo, M. Renfors, and L. Wanhammar. "Editorial: Advanced techniques on multirate signal processing for digital information processing." IET Signal Processing 5, no. 3 (2011): 313. http://dx.doi.org/10.1049/iet-spr.2011.9058.

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Yamamoto, Yutaka, Kaoru Yamamoto, Masaaki Nagahara, and Pramod P. Khargonekar. "Signal processing via sampled-data control theory." Impact 2020, no. 2 (April 15, 2020): 6–8. http://dx.doi.org/10.21820/23987073.2020.2.6.

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Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampled, the values from the discrete, sampled points are kept while the information between the sampled points is lost. Various techniques have been developed over the years to recover this lost data, but the results remain incomplete. Professor Yutaka Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.

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Li, Yiyang. "Digital signal processing techniques for image enhancement and restoration." Applied and Computational Engineering 17, no. 1 (October 23, 2023): 198–205. http://dx.doi.org/10.54254/2755-2721/17/20230940.

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Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.

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Yan, Zheng Guo, and Juan Su. "Through-Casing Resistivity Logging Signal Acquisition and Processing Techniques." Advanced Materials Research 403-408 (November 2011): 2659–62. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2659.

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Weak signal detection is the key technique in developing through-casing resistivity logging tool. In this paper, ultra-low-noise preamplifier, oversampling method, sampling integration and sampling average method, digital phase-sensitive detection technique are applied in detecting logging signals and 30nV is achieved. The indoor calibration test and field experiment of through-casing resistivity logging model machine with those weak signal detection techniques were carried out. The result showed that the measurement range of formation resistivity is 0~200 Ω.m.

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., Umashanker Sahu. "DIGITAL SIGNAL PROCESSING TECHNIQUES FOR LTI FIBER IMPAIRMENT COMPENSATION." International Journal of Research in Engineering and Technology 02, no. 10 (October 25, 2013): 168–72. http://dx.doi.org/10.15623/ijret.2013.0210024.

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Myers, D. G., Azizul H. Quazi, and Shakila A. Quazi. "Digital Signal Processing—Efficient Convolution and Fourier Transform Techniques." Journal of the Acoustical Society of America 91, no. 1 (January 1992): 536. http://dx.doi.org/10.1121/1.402719.

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Andria, Gregorio, Filippo Attivissimo, and Nicola Giaquinto. "Digital signal processing techniques for accurate ultrasonic sensor measurement." Measurement 30, no. 2 (September 2001): 105–14. http://dx.doi.org/10.1016/s0263-2241(00)00059-2.

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Eriksson, Larry John. "Active sound attenuation using adaptive digital signal processing techniques." Journal of the Acoustical Society of America 79, no. 2 (February 1986): 575. http://dx.doi.org/10.1121/1.393503.

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Conway, G. D., and J. A. Elliott. "Digital signal processing techniques for plasma dispersion curve measurements." Journal of Physics E: Scientific Instruments 20, no. 11 (November 1987): 1341–50. http://dx.doi.org/10.1088/0022-3735/20/11/006.

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Pan, Zhongqi, Junyi Wang, and Yi Weng. "Digital signal processing techniques in Nyquist-WDM transmission systems." Photonic Network Communications 32, no. 2 (January 12, 2016): 236–45. http://dx.doi.org/10.1007/s11107-015-0598-8.

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Sadik, Ahmed Ali. "FEATURE EXTRACTION IN ELECTROMYOGRAPHY BY DIGITAL SIGNAL PROCESSING TECHNIQUES." Journal of Engineering 11, no. 01 (March 1, 2005): 193–202. http://dx.doi.org/10.31026/j.eng.2005.01.17.

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Saxena, Shivani. "Digital Signal Processing Approaches in the field of Genomics: A Recent Trend." Journal of Medical Science and clinical Research 12, no. 02 (February 28, 2024): 66–77. http://dx.doi.org/10.18535/jmscr/v12i02.10.

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Digital signal processing (DSP) techniques have emerged as powerful tools in the field of genomics, enabling researchers to extract valuable insights from complex genetic data. This research paper presents a comprehensive analysis of the recent trends and advance- ments in applying DSP approaches to genomics. The objective is to provide an overview of the transformative role of DSP in genomic data analysis, variant calling, and interpretation. By leveraging DSP methods such as filtering, feature extraction, time-frequency analysis, and machine learning algorithms, researchers can enhance the quality of genetic signals, identify genetic variants, and gain a deeper understanding of genomic processes. The paper highlights key applications of DSP in genomics, including DNA sequence analysis, RNA expression pro- filing, epigenetics, and genome-wide association studies. Additionally, the challenges associated with applying DSP techniques in genomics, such as signal noise, data in- tegration, and computational complexity, are discussed. This research paper serves as a valuable resource for researchers, bioinformaticians, and geneticists seeking to harness the power of DSP in genomics, advancing our knowledge of genetic diseases and paving the way for personalized medicine and precision healthcare. Keywords: Digital signal processing, Genome analysis, Feature extraction, DNA sequence analysis, RNA expression profiling.

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Mugumya, Athur, Joan Akankunda, Emmanuel Matsiko, and Mohammed Dahiru Buhari. "A review in advanced digital signal processing systems." KIU journal of science engineering and technology 3, no. 1 (May 19, 2024): 135–44. http://dx.doi.org/10.59568/kjset-2024-3-1-14.

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Digital Signal Processing (DSP) is a powerful technology that helps in making sense of various signals, like sounds and images, using computers. This review paper explains the meaning of DSP, shows how it works to process and enhance signals. It explores the wide range of signal processing methods, categorizing them from basic noise reduction to advanced machine learning algorithms, and how they are used today to improve the quality of audio, images, medical data, and other control systems. The paper further examines into signal processing techniques, providing a comprehensive understanding of the diverse methodologies employed in DSP applications. Additionally, it addresses not only the advancements but also the drawbacks associated with advanced DSP systems, offering insightful recommendations for overcoming challenges and optimizing performance. This review also includes categories of DSP methods, providing a structured overview of the different approaches within the field. It offers a clear and concise understanding of DSP, its practical uses, and its exciting potential in the digital age

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Zhong, Meisu, Yongsheng Yang, Yamin Zhou, M. Octavian Postolache, M. Chandrasekar, G. Venkat Babu, C. Manikandan, V. S. Balaji, S. Saravanan, and V. Elamaran. "Advanced Digital Signal Processing Techniques on the Classification of the Heart Sound Signals." Journal of Medical Imaging and Health Informatics 10, no. 9 (August 1, 2020): 2010–15. http://dx.doi.org/10.1166/jmihi.2020.3127.

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Speech processing subject primarily depends on the digital signal processing (DSP) methods, such as convolution, discrete Fourier transform (DFT), fast Fourier transforms (FFT), finite impulse response (FIR) and infinite impulse response (IIR) filters, FFT recursive and non-recursive digital filters, FFT processing, random signal theory, adaptive filters, upsampling and downsampling, etc. Recursive and non-recursive digital filters are primarily deployed to absorb the signal of interest signals and to block the unwanted signals (noise). Broadly, low-pass, high-pass, band-pass, and band-stop filters are implemented for filtering functions. In frequent, the DSP theories can be used for further biomedical engineering domains like biomedical imaging (MRI, ultrasound, CT, X-ray, PET) and genetic signal analysis-cum-processing too. In this article, the experiments such as voiced/unvoiced detection, formants estimation using FFT and spectrograms, pitch estimation and tracking and yes/no sound classification are used. Also, the analysis of normal/abnormal heart sound signals using simple energy computation and the zero-crossing rate and their results are obtained. For the entire study, the Matlab R2018a tool is used to obtain the simulation results. At last, the criticism, feedbacks, comments, reactions from the student are detailed for the exceptional development of the course.

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Martinez, O., M. Parrilla, M. A. G. Izquierdo, and L. G. Ullate. "Application of digital signal processing techniques to synthetic aperture focusing technique images." Sensors and Actuators A: Physical 76, no. 1-3 (August 1999): 448–56. http://dx.doi.org/10.1016/s0924-4247(99)00028-x.

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Känsäkoski, M., O. Voutilainen, and T. Seppänen. "The Performance of near Infrared Analysers Can Be Improved by Digital Filtering Techniques." Journal of Near Infrared Spectroscopy 6, no. 1 (January 1998): 97–104. http://dx.doi.org/10.1255/jnirs.126.

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On-line near infrared (NIR) analysers are used widely for quantitative composition measurements in real-time process control systems. The accuracy and repeatability of the measurements are amongst the most important factors when evaluating the total performance of these analysers, but the lower detection limit is often limited by noise in the measurement signal. There are two major alternatives for reducing noise in an optical analyser: prevention of noise contamination and post-processing of the signal by filtering. In the second alternative, the measurement signal can be post-processed by digital filtering techniques, for example, to enhance the desired signal component. Although digital signal processing (DSP) technology offers many advantages for on-line process measurements, the behaviour of the signal must be understood thoroughly before a successful application of this technology can be developed. A digital filtering technique called matched filter was used in an experimental set-up. The performance of this filter was compared to an analog filtering of a pulse shaped signal. Experimental data were collected and filtered with a novel digital spectrometer which consists of a modulated light source, a spectrograph, a linear array detector and the analog and digital signal processing electronics needed to control and filter the signal. In this case the matched filter gave a clear improvement of 2.2–4.6 dB in the signal-to-noise ratio (SNR) relative to an analog lock-in amplifier. Among the other advantages afforded by digital filters are that they are programmable, easy to design, test and implement on a PC and do not suffer from drift. Also digital filters are extremely stable with respect to both time and temperature and versatile in their ability to process signals in a variety of ways.

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Dalmia, Hemlata, and Sanjeet K. Sinha. "Analog to Digital Converters (ADC): A Literature Review." E3S Web of Conferences 184 (2020): 01025. http://dx.doi.org/10.1051/e3sconf/202018401025.

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The signal processing is advancing day by day as its needs and in wireline/wireless communication technology from 2G to 4G cellular communication technology with CMOS scaling process. In this context the high-performance ADCs, analog to digital converters have snatched the attention in the field of digital signal processing. The primary emphasis is on low power approaches to circuits, algorithms and architectures that apply to wireless systems. Different techniques are used for reducing power consumption by using low power supply, reduced threshold voltage, scaling of transistors, etc. In this paper, we have discussed the different types and different techniques used for analog to digital conversion of signals considering several parameters.

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Benton, David M. "Photonic Processing for Wideband Cancellation and Spectral Discrimination of RF Signals." Advances in Optical Technologies 2013 (December 5, 2013): 1–8. http://dx.doi.org/10.1155/2013/738427.

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Photonic signal processing is used to implement common mode signal cancellation across a very wide bandwidth utilising phase modulation of radio frequency (RF) signals onto a narrow linewidth laser carrier. RF spectra were observed using narrow-band, tunable optical filtering using a scanning Fabry Perot etalon. Thus functions conventionally performed using digital signal processing techniques in the electronic domain have been replaced by analog techniques in the photonic domain. This technique was able to observe simultaneous cancellation of signals across a bandwidth of 1400 MHz, limited only by the free spectral range of the etalon.

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Song, Wen-Gang, Li-Jun Zhang, Jing Zhang, and Guan-Ying Wang. "Research on digital pulse processing techniques for silicon drift detector." Acta Physica Sinica 71, no. 1 (2022): 012903. http://dx.doi.org/10.7498/aps.71.20211062.

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Silicon drift detector (SDD) is a kind of high performance X-ray detector, which is widely used. The ray detection system based on SDD is composed of SDD device, preamplifier and pulse processing system. The now available pulse processing system has the problems of poor pulse pile-up rejection performance and being vulnerable to the parameter fluctuations of front-end system, which degrades the performance of detection system. A digital pulse processing system is proposed. In this system, analog-to-digital converter (ADC) directly samples the output signal of preamplifier, and transmits the data to the digital pulse processing platform for processing. According to the signal characteristics of SDD device and preamplifier, the influence of ADC sampling bits and sampling frequency on system performance is analyzed. Two optimized ADC sampling circuits are proposed to reduce energy resolution degradation induced by insufficient ADC sampling bits. The pulse shaping algorithm in the digital pulse processing system is studied. The results show that the shaping signal will not be distorted due to the parameter fluctuations of the front-end system, which proves the robustness of the digital pulse processing system. The digital pulse processing system is implemented and tested, and the correctness of the system is verified.

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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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Aitken, J. F. "Book Review: Advanced Digital Communication Systems and Signal Processing Techniques." International Journal of Electrical Engineering & Education 25, no. 1 (January 1988): 92. http://dx.doi.org/10.1177/002072098802500134.

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Martinez-Torres, M. R., F. J. BarreroGarcia, S. L. ToralMarin, and S. GallardoVazquez. "A Digital Signal Processing Teaching Methodology Using Concept-Mapping Techniques." IEEE Transactions on Education 48, no. 3 (August 2005): 422–29. http://dx.doi.org/10.1109/te.2005.849737.

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Roosnek, N. "Novel digital signal processing techniques for ultrasonic gas flow measurements." Flow Measurement and Instrumentation 11, no. 2 (June 2000): 89–99. http://dx.doi.org/10.1016/s0955-5986(00)00008-x.

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Bachal, Supriya, and Aditya Joshi. "Digital signal processing techniques to aid the deaf and mute." International Journal of Engineering Trends and Technology 17, no. 8 (November 25, 2014): 357–60. http://dx.doi.org/10.14445/22315381/ijett-v17p272.

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Dauber-Osguthorpe, P., and D. J. Osguthorpe. "Analysis of molecular dynamics simulations using digital signal processing techniques." Journal of Molecular Graphics 11, no. 1 (March 1993): 56–57. http://dx.doi.org/10.1016/0263-7855(93)85010-n.

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Parrilla, M., J. J. Anaya, and C. Fritsch. "Digital signal processing techniques for high accuracy ultrasonic range measurements." IEEE Transactions on Instrumentation and Measurement 40, no. 4 (1991): 759–63. http://dx.doi.org/10.1109/19.85348.

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Grant, P. M. "Book review: Advanced Digital Communication: Systems and Signal Processing Techniques." IEE Proceedings F Communications, Radar and Signal Processing 134, no. 6 (1987): 608. http://dx.doi.org/10.1049/ip-f-1.1987.0099.

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Fannin, P. C., A. Molina, S. S. Swords, and P. J. Cullen. "Digital signal processing techniques applied to mobile radio channel sounding." IEE Proceedings F Radar and Signal Processing 138, no. 5 (1991): 502. http://dx.doi.org/10.1049/ip-f-2.1991.0066.

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Shun-Li Lu, C. E. Lin, Ching-Lien Huang, and Tsung-Che Lu. "Power substation magnetic field measurement using digital signal processing techniques." IEEE Transactions on Power Delivery 14, no. 4 (1999): 1221–27. http://dx.doi.org/10.1109/61.796210.

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Davey, P. J., T. Donnelly, and D. J. Mapps. "Pulse slimming in magnetic recording using digital signal processing techniques." Microprocessing and Microprogramming 37, no. 1-5 (January 1993): 73–76. http://dx.doi.org/10.1016/0165-6074(93)90019-h.

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Niu, Hongjie, Hao Li, Jiufan Wang, Xiaonan Xu, and Huan Ji. "Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms." International Journal of Computer Science and Information Technology 1, no. 1 (December 30, 2023): 60–68. http://dx.doi.org/10.62051/ijcsit.v1n1.09.

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With the increase in computing power and the availability of large amounts of data, deep learning techniques, especially convolutional neural networks (CNNS) and recurrent neural networks (RNNS), have become important tools for processing complex signals. These methods show excellent performance in speech recognition, image processing, natural language processing and so on. In this paper, we explore the application of recurrent neural network (RNN) sequence algorithms in the field of computer digital signal processing, highlighting current artificial intelligence techniques and their capabilities in solving complex signal processing problems. First, the paper reviews the basic principles and development of deep learning and RNN sequence algorithms, highlighting the advances these advanced technologies have made in simulating the way the human brain processes information. The practical application and effect of RNN sequence algorithm in computer digital signal processing are demonstrated through experimental data. By comparing with traditional algorithms, we demonstrate the efficiency and accuracy of RNN in processing complex signals, such as speech recognition in noisy environments and real-time video data processing. The experimental data not only demonstrate the effectiveness of RNNS in this field, but also highlight the unique advantages of deep learning methods when dealing with large and high-dimensional data. Through these empirical studies, this paper aims to provide researchers and engineers with an in-depth understanding of the potential of RNNS in digital signal processing applications, and looks forward to the future development direction of artificial intelligence technology in this field.

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Pradhan, Manini Monalisa. "Elimination Noise from Image Using Machine Learning Techniques." Oct-Nov 2023, no. 36 (October 20, 2023): 27–36. http://dx.doi.org/10.55529/jipirs.36.27.36.

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The Image Processing system is mostly used because of their easy accessibility of powerful personal computers, bulk memory machines with graphics software and others visual application. Of “Image Processing” is applied in a number of applications. These include in area of Remote Sensing in GIS application, Medical Imaging Processing for patient care application, Forensic Studies, Textiles engineering and design, Material science, Military Research, Film industry application, and Document processing, Graphic arts. An image is defined as an array, or a matrix, square pixel arranged in rows and columns. Many image-processing procedures involve making the image as a two-dimensional signal and applying standard signal processing techniques to it. Image processing can be defined by means of a ‘digital image processing’’. The pitch of ‘digital image processing’ states to ‘processing digital’ images through channels of a computer. In this paper Image de-noising through K-SVD algorithm is presented by taking the RGB color with 256*256 sizes 24 bit standardize image.

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Al-Khazrji, Ahmeed Salam Mohammed. "Digital Signal Processing in the Frequency Domain of Audio Involves Various Steps and Techniques." June-July 2023, no. 34 (June 29, 2023): 35–39. http://dx.doi.org/10.55529/ijitc.34.35.39.

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Digital signal processing (DSP) in the frequency domain of audio encompasses a range of steps and techniques. These processes are employed to analyze, manipulate, and enhance audio signals in the digital domain. The key steps involved in DSP for audio in the frequency domain include initial conversion, frequency analysis, encoding, compression, inverse conversion, filtering, enhancement, and sound control. Each step contributes to the overall processing and improvement of audio signals, enabling efficient transmission, noise reduction, and quality enhancement in various audio applications.

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A.O., Bello, and Kabari L.G. "Digital Signal Processing for Predicting Stock Prices." British Journal of Computer, Networking and Information Technology 4, no. 2 (September 5, 2021): 12–21. http://dx.doi.org/10.52589/bjcnit-xnp3ubpl.

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With the exponential growth of big data and data warehousing, the amount of data collected from various stock markets around the world has increased significantly. It is now impossible to process and analyze data using mathematical techniques and basic statistical calculations to forecast trends such as closing and opening prices, as well as daily stock market lows and highs. The development of smart and automated stock market forecasting systems has made significant progress in recent years. Digital signal processing is required for analysis and preprocessing because of the accuracy and speed with which these large amounts of data must be processed and analyzed. In this paper, we evaluate some of these predictive algorithms based on three parameters such as speed, accuracy and complexity, we analyze the data using the dataset from kaggle.com and we implement these algorithms using pythons. The results of our analysis in this paper shows a significant correlation between the yearly prices until the year 2018 where there is a significant increase in stock price.

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Haeb-Umbach, Reinhold, Shinji Watanabe, Tomohiro Nakatani, Michiel Bacchiani, Bjorn Hoffmeister, Michael L. Seltzer, Heiga Zen, and Mehrez Souden. "Speech Processing for Digital Home Assistants: Combining Signal Processing With Deep-Learning Techniques." IEEE Signal Processing Magazine 36, no. 6 (November 2019): 111–24. http://dx.doi.org/10.1109/msp.2019.2918706.

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Baros, Jan, Vojtech Sotola, Petr Bilik, Radek Martinek, Rene Jaros, Lukas Danys, and Petr Simonik. "Review of Fundamental Active Current Extraction Techniques for SAPF." Sensors 22, no. 20 (October 19, 2022): 7985. http://dx.doi.org/10.3390/s22207985.

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The field of advanced digital signal processing methods is one of the fastest developing scientific and technical disciplines, and is important in the field of Shunt Active Power Filter control methods. Shunt active power filters are highly desirable to minimize losses due to the increase in the number of nonlinear loads (deformed power). Currently, there is rapid development in new adaptive, non-adaptive, and especially hybrid methods of digital signal processing. Nowadays, modern methods of digital signal processing maintain a key role in research and industrial applications. Many of the best practices that have been used to control shunt active power in industrial practice for decades are now being surpassed in favor of new progressive approaches. This systematic research review classifies the importance of using advanced signal processing methods in the field of shunt active power filter control methods and summarizes the extant harmonic extraction methods, from the conventional approach to new progressive methods using genetic algorithms, artificial intelligence, and machine learning. Synchronization techniques are described and compared as well.

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43

Zabunov, Svetoslav. "Digital Signal Processing in RadioSolariz Project Using SSE2." Aerospace Research in Bulgaria 34 (2022): 66–71. http://dx.doi.org/10.3897/arb.v34.e05.

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This paper aims at elaborating on the digital signal processing techniques used in data manipulation in the radioSolariz solar radio-telescope project. Focus is drawn on the implementation of different digital signal processing algorithms through the use of streaming single instruction – multiple data extensions 2. This complementary instruction set to general purpose personal computer microprocessors offers increased computational power by realizing parallel processing. The benefit is a higher data throughput while lowering the electrical power consumption of the digital signal processing computer. Optimized code fragments are shown along with original code snippets and these are discussed and analyzed. Future work and implementation of other modern parallel processing technologies are envisaged.

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Chen, Hongmei, Lanyu Wang, Jian Wang, Jiashen Li, Honghui Deng, Xu Meng, and Yongsheng Yin. "Digital Post-processing Techniques for Time-interleaved ADCs." IEIE Transactions on Smart Processing & Computing 11, no. 6 (December 31, 2022): 462–73. http://dx.doi.org/10.5573/ieiespc.2022.11.6.462.

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Wightman, Frederic, and Doris Kistler. "Application of digital signal processing techniques to research on sound localization." Journal of the Acoustical Society of America 83, S1 (May 1988): S16—S17. http://dx.doi.org/10.1121/1.2025231.

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Simoes, J. B., P. C. P. S. Simoes, and C. M. B. A. Gorreia. "Nuclear spectroscopy pulse height analysis based on digital signal processing techniques." IEEE Transactions on Nuclear Science 42, no. 4 (1995): 700–704. http://dx.doi.org/10.1109/23.467890.

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Wang, Youqi, and W. Henry Weinberg. "Ultrahigh-resolution electron energy loss spectroscopy via digital signal processing techniques." Physical Review Letters 69, no. 23 (December 7, 1992): 3326–29. http://dx.doi.org/10.1103/physrevlett.69.3326.

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Hosseini, S. Mohammad, Amir Hossein Jahangir, and Mehdi Kazemi. "Digesting Network Traffic for Forensic Investigation Using Digital Signal Processing Techniques." IEEE Transactions on Information Forensics and Security 14, no. 12 (December 2019): 3312–21. http://dx.doi.org/10.1109/tifs.2019.2915190.

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Omar, Siti Nashayu. "Application of digital signal processing and machine learning for Electromyography: A review." Asian Journal Of Medical Technology 1, no. 1 (July 30, 2021): 30–45. http://dx.doi.org/10.32896/ajmedtech.v1n1.30-45.

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This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

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A. Asker, Mshari, Khalaf S. Gaeid, Nada N. Tawfeeq, Humam K. Zain, Ali I. Kauther, and Thamir Q Abdullah. "Design and Analysis of Robot PID Controller Using Digital Signal Processing Techniques." International Journal of Engineering & Technology 7, no. 4.37 (December 13, 2018): 103. http://dx.doi.org/10.14419/ijet.v7i4.37.23625.

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Recently robotic is a playing vital role in the life In our modern society, the usage of robotic arms are increasing and much of the work in the industry is now performed by robots. As robots begin to behave like humans in an intelligent manner, control system becomes a major concern. In this paper, design and analyses of the pick and place robot due to control, the forearm, wrist, desired turntable and desired bicep is introduced to construct a closed system with four degrees of freedom (4DOFs). The main performance specifications are the accuracy and stability of the input system for obtaining a good system performance. Implementation of the control system using PID parameters for stability, minimum steady state error, minimum overshoot and faster system response has been carried out. The design of two degree of freedom PID(2DoFPID) to control robotic arm along with first order low pass filter(LPF) to compensate the unwanted signal is improved. To be able to implement such a precise and effective system, feedback system has to be made to improve the overall performance specifications. The digital signal processing controller (Arduino Uno) is used as it is active, cheap , it has open source code and easy to use in the software and hardware applications.Experimental set up developed in addition to the Matlab/Simulink implementation of the complete system. The results and the communication signals test ensure smooth operation of the control system and the effectiveness of the proposed algorithm.

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