Several techniques for Human Activity Recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid advancement of sensor technologies. However, recognizing multiple people’s activities is still challenging due to the complexity of their activities, such as parallel and collaborative activities. To address these challenges, we propose a Transformer with a Bi-directional GRU deep learning method, called TRANS-BiGRU, to efficiently learn and recognize different types of activities performed by multiple residents. We compare the proposed model with the state-of-the-art models and various deep learning (DL) models, such as Ensemble2LSTM (Ens2-LSTM), Bidirectional Gated Recurrent Units (Bi-GRU), and traditional machine learning (ML) models, such as Support Vector Machine (SVM). Our experimental results based on the CASAS and ARAS public datasets show that our model significantly outperforms the existing models for complex activity recognition of multiple residents.

The most computationally intensive part of a wideband receiver is the channelizer. The computational complexity of linear phase finite impulse response (LPFIR) filters employed in the channelizer is dominated by the number of adders used in the implementation of the multipliers. In this paper, two methods are proposed to efficiently implement the channel filters in a wideband receiver based on common subexpression elimination (CSE). We exploit the fact that a significant amount of redundant multiplications exist in the filter-bank channelizer as it extracts multiple narrowband channels from the wideband signal. By forming three and four nonzero-bit super-subexpressions utilizing redundant identical shifts that exist between a two- nonzero-bit common subexpression (CS) and a third nonzero bit, or between two nonzero-bit CS, the number of adders to implement the channel filters can be reduced considerably. Furthermore, the complexity of the adders is analyzed and design examples of the channel filters employed in the digital advanced mobile phone system (D-AMPS) and the personal digital cellular (PDC) channelizers show that the proposed methods offer considerable reduction in the number of full adders when compared to conventional CSE methods.