No abstract

No abstract

Gesture recognition (GR) has many applications for human-computer interaction (HCI) in the healthcare, home, and business arenas. However, the common techniques to realize gesture recognition using video processing are computationally intensive and expensive. In this work, we propose to task existing visible light communications (VLC) systems with gesture recognition. Different finger movements are identified by training on the light transitions between fingers using the long short-term memory (LSTM) neural network. This paper describes the design and implementation of the gesture recognition technique for a practical VLC system operating over a distance of 48 cm. The platform uses a single low-cost light-emitting diode (LED) and photo-diode sensor at the receiver side. The system recognizes gestures from interruptions in the direct light transmission, and is therefore suitable for high-speed communication. Gesture recognition accuracies were conducted for five gestures, and results demonstrate that the proposed system is able to accurately identify the gestures in up to 88% of cases.

Human activity recognition (HAR) employs machine learning for the automated recognition of motion and has widespread applications across healthcare, daily-life and security spaces. High performances have especially been demonstrated using video cameras and intensive signal processing such as the convolutional neural network (CNN). However, lower complexity algorithms operating on low-rate inertial data is a promising approach for portable use-cases such as pairing with smart wearables. This work considers the performance benefits from combining HAR classification estimates from multiple sensors each with lower-complexity processing compared with a higher-complexity single-sensor classifier. We show that while the highest single-sensor classification accuracy of 91% can be achieved for seven activities with optimized number of hidden units and sample rate, the classification accuracy is reduced to 56% with a reduced-complexity 50-neuron classifier. However, by majority combining the predictions of three and four low-complexity classifiers, the average classification accuracy increased to 82.5% and 94.4%, respectively, demonstrating the efficacy of this approach.

Intelligent reflecting surface (IRS) is considered as a promising technology for enhancing the transmission rate in cellular networks. Such improvement is attributed to considering a large IRS with high number of passive reflecting elements, optimized to properly focus the incident beams towards the receiver. However, to achieve this beamforming gain, the channel state information (CSI) should be be efficiently acquired at the base station (BS). Unfortunately, the traditional pilot estimation method is challenging, because the passive IRS does not have radio frequency (RF) chains and the number of channel coefficients is proportional to the number of IRS elements. In this paper, we propose a novel semi-blind channel estimation method where the reflected channels are estimated using not only pilot but also data symbols, reducing the channel estimation overhead. The performance of the system is analytically investigated in terms of the uplink achievable sum-rate. The proposed scheme achieves higher energy and spectrum efficiency, while being robust to channel estimation errors. For instance, the proposed scheme achieves a 80% increase in spectrum efficiency compared to pilot-only based schemes, for IRSs with N = 32 elements.INDEX TERMS Intelligent reflecting surfaces; channel estimation; massive MIMO.