###### Abstract
[19] provided a detailed analysis of the DIC measurement accuracy, considering the influence of both experimental conditions and the correlation algorithm itself. The guidelines for achieving high accuracy strain mapping have been provided too. Many of those recommendations were taken into consideration and implemented in the present work. Both DIC and IRT methods are capable of characterising the local deformation behaviour of materials by full-field measurements, albeit in remarkably different ways as they refer to different physical properties. However, they both have advantages and disadvantages, which have been well understood and documented in abundant literature (for details, interested readers are referred to comprehensive reviews [1, 19, 20]). A qualitative side-by-side comparison of DIC and IRT techniques was made in [21], where aluminium specimens were subjected to 3-point bending loading with different velocities. It was shown that both methods were capable of visualising a plastic zone quite well, i.e., the region where heat generated occurred during plastic deformation was comparable to the plastic zone determined by DIC. However, the performance of both methods differed dramatically in dependence on the strain rate: the DIC performed notably better under quasi-static conditions, while IRT revealed the plastic zone much better under dynamic loading. Despite good visible agreement between IRT and DIC images, temperature distributions cannot be mapped directly to strain distributions since both methods are footed on fundamentally different principles, and refer to physically different measured quantities--displacements and temperature, respectively. An AE technique reflecting the rapid local stress relaxation processes in solids has long been acknowledged by materials scientists as a powerful tool enabling the assessment of the dynamic behaviour of defects in real time scale, and the in situ characterisation of the deformation and fracture processes under load [22]. The method has gained popularity due to its extraordinarily high sensitivity to elementary mechanisms of plastic deformation--dislocation slip [23, 24, 25], mechanical twinning [26, 27, 28, 29, 30], temperature- or deformation-induced phase transformations [31, 32, 33, 34, 35], plastic instabilities of various kinds [36, 37, 38, 39, 40, 41, 42, 43, 44], and, of course, cracks of various types. Robust discrimination between different potential sources of AE signals is an everlasting and extremely challenging problem in the AE field. The feasibility of using various classifiers and signal- or pattern-recognition techniques to establish a basis for reliable distinguishing between different AE sources and background noise in the same dataset has been demonstrated on many materials, and several classifiers differing by classification methods, features, cost functions, robustness, etc., have been proposed and tested in the past (see, e.g., [45, 46, 47, 48]). The method proposed by [PERSON] and [PERSON] [49]) is incorporated in the present setup as a tool of choice, as will be discussed in the next section. The acoustic emission elastic waves emitted at the source and propagated towards the sensor fall in the ultrasonic frequency range, and, consequently, the AE method requires high-speed acquisition of broadband waveforms at 1-20 Msamples/s. Continuous data streaming with high acquisition rate is resource-demanding, and particularly challenging in long-term applications, e.g., during fatigue testing. The specific details of the application of AE to the fatigue crack growth monitoring will be reviewed below. Thus, the combination of the named techniques provides a rich source of detailed information about thermodynamics, kinematics, and dynamics of deformation and fracture processes occurring on different scales. As stated above, each technique is unique, adaptedto a specific purpose, and different from others in its own regard.