scholarly journals A DSP Technique for Prediction of Cancer Cell

2019 ◽  
Vol 8 (10) ◽  
pp. 1205-1209
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Cancer Cells ◽  
Dna Sequence ◽  
Digital Signal ◽  
Human Beings ◽  
Normal Cells ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Public Data

At present cancer is an alarmed disease. According to medical research, central cause of cancer is due to the genetic abnormality. Most cancers are generated due to permanent change in the deoxyribonucleic acid (DNA). For the past two decades, genomic signal processing (GSP) is a vital area of research. It has engrossed the consideration of digital signal processing (DSP) researchers for the massive amount of data accessible in the public data base. By finding out the DNA sequence for cancer cells & normal cells of human beings & applying some digital signal processing (DSP) approaches on both, difference between them can be found. Previously, discrete Fourier transform (DFT) power spectrum was used to predict cancer cells of a DNA sequence. In this paper, discrete cosine transform (DCT) and discrete sine transform (DST) approaches are presented as an alternative to analyze the spectral characteristics of cancer cells and normal cells. Further, post-processing is done using digital IIR low pass filter to improve the discrepancy between cancer and normal cells. The proposed method is tested for a number of data sets available in Gene Bank.

A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

2018 ◽  
Vol 56 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Guo Luo
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filter ◽  
Digital Signal ◽  
Band Pass Filter ◽  
Analog Filter ◽  
Bilinear Transformation ◽  
Pass Filter ◽  
Educational Method ◽  
Low Pass Filter

Signal de-noising is one of the major topics of engineering application covered in an undergraduate-level digital signal processing course. Generally speaking, it involves a number of tedious concepts that have intrinsic physical meaning, which is difficult for students to understand . In this paper, an educational method using diaphragmatic electromyographic (EMGdi) as the de-noising object, which runs on the MATLAB software, has been developed for the convenience of learning and understanding for three-years students in digital signal processing course. This method transforms the analog filter to a digital filter by applying bilinear transformation equations, which allows the students explore the various characteristics of digital filter, such as low pass filter, high pass filter, band pass filter and band stop filter. That means Laplace equation transformed by inductance, capacitance and resistance will be replaced by the z equation, which is used for deriving sequence of difference equations. In the case studies, the clinical EMGdi is used to show the features of the developed method. Furthermore, classroom experience in the Nanfang College of Sun Yat-sen University has shown that the developed method helps in consolidating a better understanding of signal de-noising processing in digital signal processing course.

scholarly journals Design and Analysis of Robot PID Controller Using Digital Signal Processing Techniques

2018 ◽  
Vol 7 (4.37) ◽  
pp. 103
Author(s):  
Mshari A. Asker ◽  
Khalaf S. Gaeid ◽  
Nada N. Tawfeeq ◽  
Humam K. Zain ◽  
Ali I. Kauther ◽  
...  
Keyword(s):  
Signal Processing ◽  
Control System ◽  
Digital Signal Processing ◽  
Digital Signal ◽  
Modern Society ◽  
System Response ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Communication Signals ◽  
Performance Specifications

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.   

Design and Realization of FIR Band-Pass Digital Filter Based on MATLAB

2012 ◽  
Vol 503-504 ◽  
pp. 228-231 ◽  
Author(s):  
Shan Ren ◽  
Xin Zhao ◽  
Jie Jiang ◽  
Dong Jin Zhao
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filter ◽  
Digital Signal ◽  
Window Function ◽  
Measured Signal ◽  
Time Interval ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Band Pass

Digital filter is one of the most important parts of digital signal processing. In practice, digital signal processing often need to limit the signal observation time interval within a certain time, choose only one period of signal that signal data will be truncated, this process is equivalent to plus window function operation to signal. In order to obtain finite unit sample response, need to truncate the infinite unit sample response sequence by window function. This paper proposes the method of using window function to design FIR Band-pass filter based on MATLAB, according to the design basic principle of FIR digital filter. Filtering processing for measured signal showed that filtering effect of the filter achieved the expected results.

Method of estimation of hidden correlation of narrowband noise signals

2019 ◽  
pp. 34-39 ◽  
Author(s):  
E.I. Chernov ◽  
N.E. Sobolev ◽  
A.A. Bondarchuk ◽  
L.E. Aristarhova
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
New Technology ◽  
Digital Signal ◽  
Measuring Instruments ◽  
Central Frequency ◽  
Pearson Criterion ◽  
Useful Signal ◽  
Band Limited ◽  
Narrowband Noise

The concept of hidden correlation of noise signals is introduced. The existence of a hidden correlation between narrowband noise signals isolated simultaneously from broadband band-limited noise is theoretically proved. A method for estimating the latent correlation of narrowband noise signals has been developed and experimentally investigated. As a result of the experiment, where a time frag ent of band-limited noise, the basis of which is shot noise, is used as the studied signal, it is established: when applying the Pearson criterion, there is practically no correlation between the signal at the Central frequency and the sum of signals at mirror frequencies; when applying the proposed method for the analysis of the same signals, a strong hidden correlation is found. The proposed method is useful for researchers, engineers and metrologists engaged in digital signal processing, as well as developers of measuring instruments using a new technology for isolating a useful signal from noise – the method of mirror noise images.

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