Signal Processing - Science topic

you can use following code to pass through the log-normal shadowing model:

s= randn(1,20); %Randomly generated signal to pass through the channel.

M=4; order of modulation (I am considering that you are using 4-QAM modulation)

s_mod= qammod(s, M); % Modulate the signal

mean= 0; %Consider mean of Log-Normal model as 0

var=1; % Consider variance of Log-Normal model as 1

h_t= lognrnd(mean, var); % Generate Log-normal model

%Befor passing through channel, add cyclic-Prefix to the modulated signal

Ncp= 5; %cyclic prefix length

s_cp= [s_mod(end-Ncp+1:end) s_mod];

% Now pass throgh Log-Normal model

r1= conv(h_t,s_cp);

%The received signal will be noisy signal, so add the noise

r= awgn(r1,10,'measured'); % Noise at SNR=5

%Now remove cyclic prefix,

y=r(Ncp+1:N+Ncp);

Dear Krishnapriya G B ,

Computer assisted analyses of cardiovascular signals facilitate timely diagnosis and treatment in critically ill patients. Different methods have been employed for the analysis and diagnosis, yet there is scope for enhancement of classification/diagnosis accuracy.

Regards,

Shafagat

answer would work. But it might not generate what you need. Have you considered including noises that are true to the body, like bodily function, sounds and surrounding RF? Eleonora Adelina Dănilă

I am working in Genomics Signal Processing ( Big-data Analysis) for the last 11 years and completed his Ph.D. in this domain itself.

Ph.D. thesis title is “Characterization of Periodicities in DNA Sequences Using Signal Processing” and the following contributions have been made by him in this domain:

(i) Journal Publications: 09

(ii) Conference Publications:04

(iii) Book Chapter:01

(iv) Ph.D. Guidance: 01 (Detection and Localization of the Hidden Patterns in the DNA Sequences Using Signal Processing-2022)

Also good knowledge of machine and deep learning algorithms.

Any researcher associated with this field can contact to me for help.

Dear Oğuzhan Memiş

x(t)----->x(nT_s) (Process of sampling which gives a discrete signal with infinite samples separated by sampling time, T_s)

To take the samples of x(t), you can simply multiply the function with Dirac(t-nT_s) {This is one of the ways to carry out this operation---process of discretization/sampling)

PS: x(nT_s) is actually a discrete signal whereas x[n] is a digital signal. x(nT_s) can be treated as x[n] if its amplitude is quantized.

Hi please go through the attached reputable research papers to clarify the concept. It is generally used in image processing and has limited application in signal processing. The above-mentioned definition from Fernando is acquired from the paper from Fikret et al. and can be a good starting point to increase your knowledge on the topic.

Fikret Işık Karahanoglu; İlker Bayram; Dimitri Van De Ville, "A Signal Processing Approach to Generalized 1-D Total Variation", IEEE TRANSACTIONS ON SIGNAL PROCESSING

Condat, L. (2013). A direct algorithm for 1-D total variation denoising. IEEE Signal Processing Letters, 20(11), 1054-1057.

hi,

i hope you are fine. is there any research or literature review available on the comparative analysis of different ensemble method used for misdiagnosis of cancer patient against xgboost method?

Dear Mr Pavlov Nikolay Pavlov

Thanks for your your input and explanation. Really appreciate it.

The article below explains the Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling. It may help you.

I'd try out some Time-of-Flight sensor (e.g. from the ST VL53-series). "Black" usually does not mean that a surface does not reflect at all, it's just a minute reflection. Might still be enough for a ToF sensor.

Take a look at "digital twin" in the context of the target city of your study (let's say city X).

Benefits of a digital twin of city X for its residents? Disaster management, energy, etc.

What are the existing infrastructure in city X, that can support development of a digital twin of the city? What is missing?

The bycycle package might be interesting for you. It is made to detect sustained oscillations and bursty ones. I've not tested it on human data personally yet (just rats), but it was made for human EEG originally.

I think it is an interesting addition to spectrum based approaches (FFT, wavelet etc) that tend to assume that your oscillation is stable over time.

Software here: https://bycycle-tools.github.io/bycycle/index.html

Paper here:

Ahmed Abdulsalam Thank you , Ahmed

Please go through the recent published articles mentioned below :

Biji, Rhea, and Vijay Savani. "Performance analysis of Vedic mathematics algorithms on re-configurable hardware platform." Sādhanā 46, no. 2 (2021): 1-5.

If your signal contains information between 0..64 Hz and 64..128Hz, and you sample it as 128Hz, the sampling process will fold-over (alias) everything in the 64..128Hz band backwards into the 0..64Hz band. So for example, a 74Hz tone will be folded over to 54Hz. A 100Hz tone will be folded over to 28Hz. (Signals above 128Hz will also get aliased into the band) So to answer your question - Yes the output of your FFT will contain information from the 64..128Hz band. But it is indistinguishable from the information in the 0..64 Hz band. If you know that there is no signal between 0..64Hz then nothing has been lost - you can fully reconstruct the signal. But if you DID have something in there, then you can't separate the two signals and they are forever combined.

why not consider using S-transform as it combines the properties of FFT and wavelet transform.

Yes, preprocessing steps changes the raw ECG signal upto some extent but this is mainly depends on the opted signal processing methods. Few methods do elimination of entire noisy components in the signal which causes huge data loss while few methods perform Denoising operation with the objective of minimal dataloss.

The Holter electronics would have to be designed to set the high bandpass filter to >10-30 Hz. Baseline wander is nothing more than a low frequency signal and the monitor has no clue that it is not a physiologic signal. ECG recorders allow baseline wander to be displayed because some of the components of the ECG are low frequency, like the T wave. Therefore, most ECG recording devices use a fairly low high-band-pass filter, around 0.15 Hz.

It depends on what you are looking for as to whether you can get away with raising the high-band-pass filter. Intracardiac signals recorded during an electrophysiology study typically are filtered 30 Hz to 500 Hz, in which case there will be no baseline wander. The recorded signals, however, will have only high-frequency characteristics, so they will look very sharp and spikey. If that doesn't matter to you, that's your answer

Good answer by Aparna Sathya Murthy

Dear Kenan Morani .

Numerous data from the big data are often represented by graphs. Once vector data is associated with their nodes, one obtains what is called a graph signal. The processing of such graph signals faces several challenges because of the nature of the underlying information; combining graphs theoretical aspects with signal processing methods.

Regards,

Shafagat

Ilyas Khan

Image processing can benefit from the usage of discrete wavelet transformations. As image resolution grows, so does the amount of storage space required. DWT is used to reduce the size of an image without sacrificing quality, resulting in higher resolution.

I also recommend that you read the following articles:

1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5130972/

2. https://www.sciencedirect.com/topics/physics-and-astronomy/compressed-sensing

3. https://www.oatext.com/Medical-image-compression-framework-based-on-compressive-sensing-DCT-and-DWT.php

There are several works in the literature dealing with this problem. The challenge is that the RSSI measurements are noisy and there exists some variability between consecutive measurements even if the master and slave are completely static.

One approach is to use the Log-Distance Path Loss (LDPL) model to transform the RSSI into a distance, more information at https://en.wikipedia.org/wiki/Log-distance_path_loss_model , but it requires a proper calibration for your environment and devices. Even with a proper calibration, it might fail indoors because of several signal propagation issues.

A colleague is currently working in this problem, you may find more information at:

Best regards,

Joaquín.

I suppose this depends on what kind of obstacles you are considering and how they are affecting the signals. If you consider an object that is scattering the signal, then the pathloss will be proportional to (d_1*d_2)^2 where d_1 is the distance from the transmitter to the obstacle and d_2 is the distance from the obstacle to receiver. For a given total propagation distance d_1+d_2, it follows that the pathloss is at its smallest when the scattering object is close to the transmitter or the receiver.

Thank you very much.

@Anil Kamboj

@Qamar Ul Islam

Hello, Nils Wagner .

What are you trying to achieve? Wouldn't be an acceleration signal sufficient for your application? Double-intergation of the acceleration signal to the displacement is a highly unreliable process, as pointed out by Rolf Schirmacher .

If you need to estimate a total displacement (i.e. a static part of the signal), be vary that you don't know initial conditions (especially initial velocity) and there is practically no method to evaluate them based on acceleration data. There is also noise but it can be at least partially resolved by some smoothing/filtering.

Also note that Phyhox claims that "errors in the acceleration sum up in the velocity and hence have an even worse impact on the location. Usually, the noise of the sensors brings the results to absolutely unreasonable values within a short time."

Stomach cancer is cancer that may affect any part of the stomach and extend to the esophagus or small intestine, and it causes the death of nearly one million people annually. It is more prevalent in Korea, Japan, England and South America. It is more prevalent among men than women. It is associated with eating too much salt, smoking, and also low intake of fruits and vegetables. Therefore, it is believed that its spread in countries such as Korea and Japan is due to the consumption of salted fish mainly by Koreans and Japanese, as well as the use of canned food and food preservatives. Mucosal colonization of H. pylori is believed to be the main risk factor in about 80% of stomach cancers

Stomach cancer is diagnosed through an endoscopic examination that allows a biopsy to be extracted from the affected tissue, and then analyzed to confirm the presence of a tumor. Dr. Riccardo Rosati, a specialist in gastroenterology at San Raffaele Hospital in Milan, says, "Before undergoing treatment, the patient needs to do a series of other ultrasound and other examinations to check the areas, glands and organs covered by the disease, in order to determine the degree of its progression.

As a researcher, I believe that stomach cancer cells do not send messages to the brain due to the lack of associated neurons

Just wild guessing...

Kullback-Leibler divergence? ( transforming a bit the data )

In R programming there is a function to do it called KLD. I guess you can do it in python as well.

you can see https://sigport.org/documents/how-derive-bias-and-mean-square-error-estimator

Arkadiy Prodeus Thank you so much. I have solved this problem with Hilbert transform.

Dear Douglas Diehl

These articles might be useful, have a look:

1.

2.

Kind Regards

you can use DSP tool in Matlab.

Dear Emanuele Tavanti

These articles might be useful, have a look:

1. https://www.mdpi.com/2076-3417/10/23/8484/pdf

2. https://apps.dtic.mil/sti/pdfs/ADA606230.pdf

3.

Kind Regards

Old news but I found it here (http://sparselab.stanford.edu/atomizer/) if anyone finds this post while searching for it.

I've also downloaded the ZIP for posterity. Anyone feel free to get in touch if you need it. I plan on keeping it forever.

Hi Kareem,

I haven't worked brain signals. However I would suggest you try HOSA toolbox to work on non-stationary signals https://in.mathworks.com/matlabcentral/fileexchange/3013-hosa-higher-order-spectral-analysis-toolbox

Especially, you can try cumulants or bispectrum techniques.

Dear Alex Minetto ,

we worked on the design and implementation of of a GPS receiver for the sake of extracting the one PPS, we could realize the signal acquisition phase and the tracking phase where we could generate a copy of the carrier with reduced frequency and of the pn conde clock generator. BY dividing these signal with appropriate division ratio one can get the one PPS. Its stability was to be evaluated. But the last divider stage is not yet realized and we plan to realize it and after that evaluate its accuracy and stability. please see the papers:

Once achieved I will notify you.

Best wishes

This criterion has some advantages:

It does not have unit. It is a coefficient. Therefore, you can compare different signals.

I think it stems from the coefficient of variations (CV). It is the inverse of square of CV. CV is a standardized deviation index.

Application of graph signal processing (GSP) tools to various sensor processing tasks include sensing, filtering, sensor data classification/clustering, anomaly detection, and prediction. There are also several issues related to time-series signals as well as image, video, and heterogeneous signals.

GSP help represent irregular data structures on graphs, extends classical digital signal processing (DSP) to signals on graphs by combining algebraic and spectral graph theory with DSP and offers a potential solution to numerous real-world problems involving signals defined on topologically complex domains, e.g., social networks, point clouds, biological networks, environmental and condition monitoring sensor networks, etc. Hence, GSP tools can deal with various sensing tasks, including, but not restricted to sampling, distributed filtering, denoising, data processing tasks, smart grids and infrastructure health.

here is my article which can be an answer of your question

Dear Bidyut Saha

These articles might be an asset, have a look:

1. https://www.mdpi.com/1424-8220/21/7/2287/pdf

2. https://www.mdpi.com/1424-8220/20/4/1210/htm

3.

Kind Regards

Dear Gerhard Martens ,

After seeing your and other respectable researchers' answers, I am glad I asked this question.

I am really delighted to hear from you the history of an ever-lasting discussion about sensor and actuator definitions. I have always found it annoying that the sensor definition has usually been preferred as a "too specific" definition to serve only for an interface of an electrical/electronic system and an "other" system/medium with different form of signal(s).

Besides, that diiscussion, I can start another one:

There are many commercial integrated devices which are called "sensor"s, although in fact they are not basic sensors but are more complicated small systems which may also include electronic amplifier(s), filter(s), analog-digital-converter, indicators etc. For sure, these are very convenient devices for electronic design, but I think it is not correct to call them "sensor". Such a device employs a basic sensor but besides it provides other supporting electronic stages to aid the electronic designer. I don't know if there is a specific name for such devices.

Thank you again for your additional explanations.

Best regards...

Thank you so much for your inputs, I will go through them and see if I can leverage it.

Generally if the EMG signal frequency you are dealing with are present in higher frequencies, then the best option is to use filter 70-300Hz.

Based on your work you can use MVC, another normalization technique.

MVC technique used in this :

Hi Sarah

The Microgrid controller design and its' robustness testing is different from communication system or control system. The white noise concept will not work here. The controlled Microgrid testing depends on operational scenarios and several robustness metrics are proposed by researchers for those scenarios.

One of the testing protocol is published by IEEE Standards--

2030.8-2018 - IEEE Standard for the Testing of Microgrid Controllers

DOI: 10.1109/IEEESTD.2018.8444947

It is useful to simulate operational scenarios and testing of designed controlled Microgrid.

Hi Mohammed,

thank you for your quick answer. Generally, vibrometer measurements on rotating objects are more difficult compared to stationary vibrating objects. The major drawback is the increased noise level, caused by speckle effects due to the object rotation in combination with the surface roughness. "Speckle noise" is increasing with rpm rate and concentrates in "speckle peaks" at multiples of the rpm rate, leading to a typical comb-sprectrum. The amplitudes of the speckle peaks are nearly constant over the measurement band width. So it is much harder to detect a vibration , especially when it is correlated with the rpm rate, which is mostly the case. In that case the vibration signal is on top of a speckle peak. If the vibration amplitude is big enough (say 3 xor 5 x of the speckle peak height), then its easy to detect. But when the vibration amplitude is in the same range as the height of the speckle peak, it is nearly impossible to detect. Furthermore, an investigation of the vibration signal in time domain is virtually impossible- independant if you look at velocity or displacement data, because of the high noise level. Especially the displacement signal will additionally increase or decrease continuously, because the vibrometer will acquire the part of the circumferential veloctiy, which is projected on to the laser beam.

Mohammed, this is a how-long-is-a-piece-of-string question. It is very difficult to give a useful answer without knowing more details, such as:

1. What is the scale of machinery you are studying? Power stations and mega-ships, micromotors or something in between? If you are studying non-portable systems, you will need a portable lab. If you are studying large systems, the rpms will be lower, which affects your choice of suitable accelerometers and signal analysers. If you are studying tiny systems where the vibrations are small, you may need vibration isolation platforms to get usable measurements.

2. How many people does the lab need to accommodate and how many experiments do you plan to run at once? Is the lab for research only or will it also be used for teaching?

3. Do you want to study the effects of loading? If so, you will need to consider braking devices, dynamometers or other forms of loading systems.

4. Laboratories usually provide calibration services, at the very least for the lab users, but often for outside clients as well. What level of calibration do you intend to provide? Do you just want secondary calibration to verify that your equipment is operating correctly or do you want primary calibration to check the secondary calibrators? Calibration and traceability are important in any research work, but they are particularly important in any forensic or expert witness work.

5. Do you need ventilation and/or cooling systems for combustion engines?

6. Do you need soundproofing for noisy equipment or vibration isolation for shaky equipment to avoid disturbance to other building occupants?

7. Do you plan any destructive testing? If so, you may need safety enclosures and other safety equipment.

8. What is your budget? How much floor area do you have? Are you adapting an existing lab space or creating a new one from scratch?

9. Do you plan to make acoustic measurements as well as vibration measurements?

10. Vibration studies may need to measure the modes of vibration as well as the amplitudes. High-speed video and/or strobes may be needed for this. Laser interferometry may also be needed. Appropriate lighting will be needed for video work.

Dear Delaram Rabiei,

In addition to what is proposed above, i suggest you to see links and attached files on topic.

Best regards

Actually NIST Test suite consists of a bundle tests. Hence, you need not write code for all these tests to find the Randomness of the image. The code for all these tests are given in NIST web site. You need to download the code and run using Eclipse or any other IDE. It will be simple and useful. Only thing you should know that the procedure to the run the different tests.

Hi Hobey Tam and Denise Sogemeier !

I have been working on signal processing since last year and I agree with you, I have not found like a common protocol for sEMG processing. Sometimes the processing stage depends on the signal acquisition process and the characteristics you want to find. After looking for a paper which contains a certain processing procedure, I found these two that could be helpful to give you an idea about where to start. I hope so! Greetings from Mexico!

Interesting recommendations

Thank you for your suggestions, Vadym Slyusar ! I will have a look on it.

Hello dear friend Wassim Diai

I hope the next code to calculate the Shanon entropy of given data will be helpful in your work

Wavelet coefficients (cD1,cD2,....A6) will be the entire data.

python 3.7 is used to implement shanon entropy.

pandas library is imported as pd.

Good job

import pandas as pd

data = [3,6,7,12,5,7,.....] #you insert your data here

pd_series = pd.Series(data)

counts = pd_series.value_counts()

entropy = entropy(counts)

print(entropy)

Narasim Ramesh Thank you again for your response. So, we must compute the sum of squares of ai and di for each layer of decomposition and the layer with lowest value of the sum is the required level we are looking at? How do we compute it at the different levels? For example, if we look at the level A8, what actually are the values of A8 and D8? I mean should we compute it by looking at those graphs or is there any formulation? How do we check that?

From one of the best group in the field :

There are several reasons that can cause fluctuations in training loss over epochs. The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. This is why the batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters. If you use all the samples for each update, you should see it decreasing and finally reaching a limit. Note that there are other reasons for the loss having some stochastic behaviour.

This explains why we see oscillations. But in your case, it is more than normal I would say. Looking at your code, I see two possible sources.

Hi,

You may use the following python code to create T3 from S2 matrix. Since, you have mentioned the sensor is monostatic, considering the reciprocity constraint S12 = S21, the code should produce the required output. Find the attached formulation and python file.

Good luck with polarimetry :-)

# -*- coding: utf-8 -*-

"""

Created on Mon May 3 09:00:21 2021

@author: Narayana

"""

import numpy as np

S11_real = 1

S11_imag = 0

S21_real = 0

S21_imag = 0

S22_real = 1

S22_imag = 0

# Scattering matrix

S2 = np.array([[S11_real+1j*S11_imag,S21_real+1j*S21_imag],

[S21_real+1j*S21_imag,S22_real+1j*S22_imag]])

# Kp- 3-D Pauli feature vector

Kp = np.expand_dims(np.array([S2[0,0]+S2[1,1], S2[0,0]-S2[1,1], S2[1,0]]),axis=1)

# 3x3 Pauli Coherency Matrix

T3 = np.matmul(Kp,np.conj(Kp).T)

More samples are generally better until such point as the difference in the real signal between samples is smaller than the quantization or other noise. At that point, especially with quantization, it may be a point of diminishing returns.

The other thing that nobody mentions is that faster sampling means less real-time processing time. In many systems, it's not really an issue as the time constants of the physical system are so slow as to never challenge the processing. In others, say high speed flexible meachatronic systems, the required sample rates may challenge the number of processing cycles available to complete the task.

Generally, the best bet is to return to the physical system's time constants and (if possible) sample 20-100x as fast as them.

@Anders Buen Power Spectral Density

Exactly Wassim Diai . You may experiment with this approach for both 1D and 2D convolutions as well.

Accuracy can be seen as a measure of quality. High accuracy means that a rule gives more relevant results than irrelevant ones, and a high recall gives many relevant results on whether or not rules invalidity is returned.

Denis Muhra

Its always better to use ensemble of features

Use FFT and mean both as classification features. Also explore Cepstrum.

in Fourier analysis, the cepstrum is the result of computing the inverse Fourier transform of the logarithm of the estimated signal spectrum. The method is a tool for investigating periodic structures in frequency spectra. The power cepstrum has applications in the analysis of human speech.

This can be used here too based on pedal length and leg length of each person.

and strength/ intensity of strokes.

Dear Daniel Prado de Campos ,

Look the ijnk, mayde useful.

Regards,

Shafagat

Each wavelet base has different spectral behavior, therefore, different impulse response for the same scales. You must choose the one that best reflects the spectral characteristics of the analyzed signal. I particularly like using the Morlet wavelet, because it is easy for you to adjust the center frequency and bandwidth to better analyze your signal.

Piyush Kumar For generating coherency matrix and further performing target decomposition, Single Look Complex SAR data is required which is Slant Range Data Product. So, in case of Chandrayaan-2 DFSAR data, you need to explore SLI (Single Look Image) data. I think that these basic information are available at ISSDC web portal where you might be downloading data sets.

Agreeing with Pascal Salart, but making it simple. One signal is x(t), the other is y(t), you study the covariance matrix E(x(t+i) y(t+j)), which can be estimated by low pass filtering vectors v(t, i) = (x(t+i-N), ... x(t+i))

and w(t, j) =(y(t+j-N),... y(t+j)) with N a length of observation window,

then the scalar product <v(t, i), w(t, j)> estimates the mean expectation E() above.

The matrix obtained is a Gram matrix, hence it diagonalises with eigenvectors, eigenvalues obtained by Gram-Schmidt algorithm. QED...

Ok ?

Aparna Sathya Murthy I came across this and tried to install this in google colab (pip install pyeeg), but it says:

ERROR: Could not find a version that satisfies the requirement pyeeg (from versions: none)

ERROR: No matching distribution found for pyeeg

It might interest you that there is a possibility to use complexity measures to assess the state of the observed complex system and make decision about arrhythmias.

An example how to do it can be bound in our paper on prediction of TdP arrhythmias from ECG recordings. Everything is explained in the paper in detail. The final version will contain rewritten entropy section and substantially improved methods, intro, etc.

Back to your question. Complexity measures when applied wisely enable us to substantially reduce the complexity of complex systems under the observation. This includes biosignals along with ECGs, EEGs, etc.

Hopefully this will enable you to orientate yourself in this exciting, yet quite complicated area of research.

Utkarsh Singh There is an esthetic reason in why a mathematical method is of interest in signal processing:

-a beautiful algorithm is well articulated, says what it does in few instructions, and does it in a stable and reliable manner

-this hints to the underlying algebra

With powerful and minimal computation, we go deep into algebra structures: group, rings, fields (see references on Evariste Galois as the inventor of "group" as we know it)

-Fourier transform is an interesting invention: it allows to decompose a signal into resonating modes (as for piano music: you produce a sound at frequency F, but also its harmonic NxF...). Naturally there is the aliasing question and the Nyquist theorem for reconstruction

There are many more time-frequency representations: Fourier, Laplace, discrete or continuous, cosine transform, wavelet transform, etc.

The interesting feature of discrete algorithms for those transforms is that you can implement a butterfly structure.

The key idea is to replace a very large number of multiplications (in brute force "non-esthetic" programming) by a smaller number of additions.

This idea worked for me for developing a codec system using underlying GF(n) properties.

See this patent:

The regularity in the processing and the efficiency of the representation go hand in hand.

Let me go back to a very basic mathematical method: the Gram-Schmidt decomposition: take a sequence of n vectors v(1),..., v(n), and the matrix of cross-products m(i,j)=<v((i),v(j)>. The Gram-Schmidt method diagonalises this matrix. It extracts eigenvalues, and eigen vectors. In frequency terms, it extracts modes (resonating modes present in the signal).

This algorithm highlights the efficiency side of the representation: it's projecting the signal onto something found "in itself", call it principal components if you want.

There are only two reasons for choosing a technique in engineering:

-(i) it addresses the problem completely

-(ii)it's economically implementable.

Both criteria are equally important and a good way to find these is to look for elegant, esthetic solutions (minimal and complete at the same time).

Does it help?

good luck

Serial polling is associated with computer-interface aspect of the instrument. The interface connectors are put on the back-side of the instrument (RS-232 or IEEE-488). If the error occurs when the instrument computer-interface is active, then it can be due to error in the interface program in the computer.