Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

In this paper we present a convolutional neuralnetwork (CNN)-based model for human head pose estimation inlow-resolution multi-modal RGB-D data. We pose the problemas one of classification of human gazing direction. We furtherfine-tune a regressor based on the learned deep classifier. Next wecombine the two models (classification and regression) to estimateapproximate regression confidence. We present state-of-the-artresults in datasets that span the range of high-resolution humanrobot interaction (close up faces plus depth information) data tochallenging low resolution outdoor surveillance data. We buildupon our robust head-pose estimation and further introduce anew visual attention model to recover interaction with theenvironment. Using this probabilistic model, we show thatmany higher level scene understanding like human-human/sceneinteraction detection can be achieved. Our solution runs inreal-time on commercial hardware

Massive MIMO with I/Q imbalance: Performance analysis and compensation

In this paper, we consider the uplink of a single-cell massive multiple-input multiple-output (MIMO) system with inphase and quadrature-phase imbalance (IQI). This scenario is of particular importance in massive MIMO systems, where the deployment of lower-cost, lower-quality components is desirable to make massive MIMO a viable technology. Particularly, we investigate the effect of IQI on the performance of massive MIMO employing maximum-ratio combining (MRC) receivers. In order to study how IQI affects channel estimation, we derive a new channel estimator for the IQI-impaired model and show that IQI can substantially downgrade the performance of MRC receivers. Moreover, a low-complexity IQI compensation scheme, suitable for massive MIMO, is proposed which is based on the IQI coefficients' estimation and it is independent of the channel gain. The performance of the proposed compensation scheme is analytically evaluated by deriving a tractable approximation of the ergodic achievable rate and providing the asymptotic power scaling laws assuming transmission over Rayleigh fading channels with log-normal large-scale fading. Finally, we show that massive MIMO effectively suppresses the residual IQI effects, as long as, the compensation scheme is applied.