Raw data clearly contains an ECG channel. Yet raw.info shows ECG channels as 'Not available'.
Use raw.set_channel_types
Eg.) raw.set_channel_types({'ECG1-ECG2':'ecg'})
Related
In this dataset, I wanted to use signals with same unit and different SNR as input signals in ICA, i.e.
ica_input = np.array([ record_118e_6(MLII),
record_118e00(MLII),
record_118e06(MLII),
record_118e12(MLII),
record_118e18(MLII)
])
Is this a correct input to ICA?
Can I here consider the signal with different SNR linear mix of noises and true signal?
According to this article (which states how the dataset is generated by nst), The above input channels are linear mix of noise and clean signal and from plotting one can see that this data is clearly non gaussian hence ICA can be used in this case. Please correct me if I am wrong.
For Face Recognition I am using KNN based approach as per link
Training dataset
Bollywood Actors: Total Actors 50 and for each actor 100 images are
there
Bollywood Actress: Total Actress 50 and for each actor 100 images
are there
For Testing this model I am using Real time Youtube Videos and link of one of the video
With correct detection large number of misclassifications are also coming and as video length increases misclassifications are also increasing
Result of Above video
Correct detection
Boman_Irani
Amitabh_Bachchan
Incorrect detection
Sridevi
Virender _Sehwag
Nishant _Chaturvedi
Mausam _Khatri
Uttam _Kumar
IS THERE ANY WAY TO REDUCE/REMOVE THIS WRONG DETECTION
Update:
I've tried these two examples.
https://github.com/ageitgey/face_recognition/blob/master/examples/face_recognition_knn.py
https://github.com/davidsandberg/facenet
I am using ti mmwave 1642 evm sensor for generation of pointcloud data. For processing the data, I am using Intel NUC.
I am facing the problem of converting pointcloud data from mmwave sensor to laserscan.
By launching rviz_1642_2d.launch, I am able to see pointcloud data in rviz.
How to convert the pointcloud data, generated from mmwave sensor, to laserscan?
First of all, this conversion is not straight forward since a pointcloud describes an unordered set of 3d points in the world. A laser scan, on the other hand, is a well parametrized and ordered 2d description of equiangular distance measurements.
Therefore, converting a pointcloud into a laserscan will cause a massive loss in information.
However, there are packages like pointcloud_to_laserscan which does the conversion for you and furthermore, you can define how the conversion should be applied.
I have extracted all the features say about 15 features,for medical images,now i need to classify them as Normal (0) or abnormal (1). how can i prepare training data for these images? I tried using ground truth for testing data, is it the right way of doing it?
If you use neural networks(convolutional neural networks to be more precisely ) , the pixels themselves are the features so the preparations(pre-processing) its just related to things like:
Resolution reduction
Artificial data creation from original training set(adding some distortions , reflections, and variations to the original data)
Normalization(to get almost 0 mean and low variance) for example, if your data is one channel depth(greyscale) ranging from 0 to 255, you can do something like:
data = (data - Mean(data))/255
I am working in research where we are using smart-phone camera to monitor users heart-rate using color variation as signal.
What I did is getting the red color channel every 0.1 second (10Hz).
The problem is that I am trying to use an FFT to get different frequencies that exist in the extracted signal and I used this Java code where the FFT function get as input two arrays (one for real part and one for img part of complex numbers).
I saw also from this post that I can compute frequencies from the FFT function's results by using the formula:
freq = i * Fs / N
where Fs is the sampling rate and N is the number of points(input).
The problem is that my sampling rate Fs, is too low (10Hz) and if I use above formula I am getting very low frquencies. Is there any other way to get frequencies?