A modified indoor path loss prediction model is presented, namely, effective wall loss model. The modified model is compared to other indoor path loss prediction models using simulation data and real‐time measurements. Different operating frequencies and antenna polarizations are considered to verify the observations. In the simulation part, effective wall loss model shows the best performance among other models as it outperforms 2 times the dual‐slope model, which is the second best performance. Similar observations were recorded from the experimental results. Linear attenuation and one‐slope models have similar behavior, the two models parameters show dependency on operating frequency and antenna polarization.

This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots.

The scattering parameter formulation for the envelope correlation in an(N,N)MIMO antenna array has been modified to take the intrinsic antenna power losses into account. This method of calculation provides a major simplification over the use of antenna radiation field patterns. Its accuracy is illustrated in three examples, which also show that the locations of the correlation minima are sensitive to the intrinsic losses.

Recent research on the performance of multicarrier systems suggest that the us e of wavelet transforms (WT) outperforms the conventional fast Fourier transform (FFT) orthogonal frequency division multiplexing (OFDM) systems. Wavelets based systems provide better spectral efficiency because of no cyclic prefix requirement, have very narrow side lobes and also exhibit improved BER performance. Meanwhile, channel estimation in wavelet based multicarrier systems still remains a big challenge. In this work an investigation into the performance of discrete wavelet transform based multicarrier system using zero forcing equalization in time domain is presented. The results produced were then compared to that of conventional FFT-based OFDM system. The proposed system shows superior BER performance.

This paper presents a fast and accurate system to determine the type of blood automatically based on image processing. Blood type determination is important in emergency situations, where there is a need for blood transfusion to save lives. The traditional blood determination techniques are performed manually by a specialist in medical labs, where the result requires a long time or may be affected by human error. This may cause serious consequences or even endanger people’s lives. The proposed approach performs blood determination in real-time with low cost using any available mobile device equipped with a camera. A total of 500 blood samples were processed in this study using different image matching techniques including oriented fast and rotated brief (ORB), scale invariant feature transform (SIFT), and speed-up robust feature (SURF). The evaluation results show that our proposed system, which adopts the ORB algorithm, is the fastest and the most accurate among the state-of-the-art systems. It can achieve an accuracy of $99.6\%$ in an average time of 250 ms.