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How can we tell if an ensemble of data (SD measurements) is good or not?
This is an ensembles of measurements in which I am considering for my thesis, and I don’t know which method/technique/techniques are most indicated/suitable (or any specific techniques/techniques) to analyze this „set” of data, which is the following.
Here are some comments about the above image:
The same SD measurements at different intervals were taken at the same probe
The only change in the readings were the temperature
The SD measurements are not all the same
I also did additional SD readings inside a magnetic field of the same samples and in between different temperatures. All the results were the same in any case.
That’s all, I didn’t want to confuse or „overload” you.
So, now I need to know how to proceed with this project, with any technique/techniques which I can use.
A:
There are a lot of possible answers to this question. As a start you might want to try Principal Component Analysis. It’s useful to think of any set of data as being a cloud of points that can be described (by central tendency, variance, etc.) by a few vectors (that are often called principal components). Not all possible directions in „space” are well-explored because new data sets are always being gathered and the information we have about the space is always increasing. Nonetheless, these principal components can be found by exploring the space in a way that best describes our data.
So, as an example, say I have this data:
$$
\begin{array}{c|c}
x & y \\
Lab: Binaural Head-Related Transfer Function Detection and Measurement We can now more accurately design multi-channel real time headphones, and develop binaural sound reproduction systems. Based on the ideal head-related transfer function (HRTF) measured on a head simulator, we have designed a portable head-related transfer function (HRTF) measuring device. This device, which is small and light, can be used widely for both HRTF measurement and HRTF correction. Also, our device is a true original product made in Japan as well as the license of the original device, the head-related transfer function measuring system (CR-04). To begin with, we measured the HRTF of 30 listeners using a portable device, and we obtained the transfer functions that we can call the „HRTF characteristics”. On the basis of the measured transfer functions, we created head models using the advanced calculation formula by adjusting the head diameter, and designed a multi-channel headphone system by simulating acoustic situations on the basis of the designed head models. The transfer functions were well fitted by the elliptical model transfer function using this device. In particular, the transfer functions of elderly listeners were well fitted, and in this device, the HRTF was correctly measured, which was not previously achieved with the CR-04. HRTF measuring device was developed for future studies on how the human auditory system, in particular the low-frequency region, recognizes sound sources. We now believe that our device can be used broadly for both HRTF measurement and HRTF correction. Performance The measuring module consists of three parts, a microphone, a filter and a digital-to-analog (D/A) converter. The headset with the measuring module is illustrated in the figure below. The measuring module uses a tube microphone with a highly sensitive/high sound pressure sensor, which generates a frequency-independent signal and a sinusoidal signal at low frequency; at high frequency, the frequency response of the microphone is flat and the phase is linear. The measuring module is divided into two parts, a microphone and a filter, and each part is constructed so that the microphone does not pick up noise generated by the device or within the room. Each part consists of an amplifier and a filter. The device is connected to a notebook via two USB cables. Headphone PC software enables two-way communication through the USB. When PC software and microphone is connected, the microphone is connected via a mixer into the 648931e174
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