Intensive care units (ICUs) are busy and noisy areas where patients and professional staff can be exposed to acoustic noise for long periods of time. In many cases, noise levels significantly exceed the levels recommended by the official health organisations. This situation can affect not only patient recovery but also professional staff, making ICUs unhealthy work and treatment environments. To introduce the measures and reduce the acoustic noise in the ICU, acoustic noise levels should first be measured and then appropriately analysed. However, in most studies dealing with this problem, measurements have been performed manually over short periods, leading to limited data being collected. They are usually followed by insufficient analysis, which in turn results in inadequate measures and noise reduction. This paper reviews recent works dealing with the problem of excessively high noise levels in ICUs and proposes a more thorough analysis of measured data both in the time and frequency domains. Applied frequency domain analysis identifies the cyclic behaviour of the measured sound pressure levels (SPLs) and detects the dominant frequency components in the SPL time series. Moreover, statistical analyses are produced to depict the patterns and SPLs to which patients in ICUs are typically exposed during their stay in the ICU. It has been shown that the acoustic environment is very similar every night, while it can vary significantly during the day or evening periods. However, during most of the observed time, recorded SPLs were significantly above the prescribed values, indicating an urgent need for their control and reduction. To effectively tackle this problem, more detailed information about the nature of noise during each of the analysed periods of the day is needed. This issue will be addressed in the continuation of this project.

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Alamouti space time block code (STBC) has been a revolutionary technology in multiple-input multiple-output (MIMO) wireless communication since it provides full transmission diversity. To reduce a multi-path effect and a consumed power, the dynamic beam-forming technique should be used to enable antennas focusing on a particular area. The aim of this paper is how to reduce the computational complexities of independent component analysis (ICA) and speed up the algorithm used in estimating the direction of arrival (DOA) angles. First, we derive a simple formula to reduce the number of unknown DOA to be one only. Then, real-imaginary (Re-Im) decomposition for MIMO system is used to reduce the computational complexities of ICA algorithm.The novel criteria used in this paper is that the kurtosis measuring for the extracted source will be minimum at one of the unknown values of DOA angles. Finally, particle swarm optimization (PSO) will be used as an effective tool to locate the DOA angle positions that minimize the kurtosis measuring. Performance analysis of the proposed approach with QPSK Alamouti STBC in MIMO channel was implemented using MATLAB. The validated criterions for the new approach were first examined. Then, root-mean-square-error (RMSE) was employed to test the proposed approach at different SNR levels. The main parameters that influence on this approach were evaluated. It was found that superior performance could be obtained at ΔDOA > 10 0 when antenna spacing set to be λ/2 using at least 10 3 snapshots. The important point observed during simulations was computational complexity (and latency) of the proposed approach was reduced to the minimum by employing Re-Im decomposition model and PSO algorithm. Consequently, this approach is very efficient for hardware implementations.