Compressed Sensing is new method of data acquisition. Typical data acquisition approach is based on the measurement of signals with a frequency determined by the sampling theorem. Data sampled in this way are often redundant. Therefore it is necessary to compress it, often in a loss manner (the perfect examples are image compression algorithms such as JPEG), in order to reduce the amount of data to be transported or stored. Doing so causes that immediately after the measurement, part of the data is rejected. Compressed Sensing is sensing protocol that minimizes the amount of required measurements during sensing. It is designed to measure only the essential components of the signal, omitting redundant information. It is possible to apply such protocol when additional conditions are fulfilled: sparseness of the signal as well as incoherence during sensing.

Compressed Sensing is new method of data acquisition. Typical data acquisition approach is based on the measurement of signals with a frequency determined by the sampling theorem. Data sampled in this way are often redundant. Therefore it is necessary to compress it, often in a loss manner (the perfect examples are image compression algorithms such as JPEG), in order to reduce the amount of data to be transported or stored. Doing so causes that immediately after the measurement, part of the data is rejected. Compressed Sensing is sensing protocol that minimizes the amount of required measurements during sensing. It is designed to measure only the essential components of the signal, omitting redundant information. It is possible to apply such protocol when additional conditions are fulfilled: sparseness of the signal as well as incoherence during sensing.