Sorry, you need to enable JavaScript to visit this website.

This paper presents a novel neural module for enhancing existing fast and lightweight 2D human pose estimation CNNs, in order to increase their accuracy. A baseline stem CNN is augmented by a collateral module, which is tasked to encode global spatial and semantic information and provide it to the stem network during inference. The latter one outputs the final 2D human pose estimations.

Simultaneous wireless information and power transfer (SWIPT) is a key technology for enabling future high-tech lifestyles by guaranteeing the perpetual operation of trillions of low-power IoT devices. Currently, reconfigurable intelligent surface (RIS) is a promising technology for achieving cost-effective and energy-efficient wireless technologies. In this paper, we propose an efficient beam-sharing algorithm for RIS-aided SWIPT systems.

Simultaneous wireless information and power transfer (SWIPT) is a key technology for enabling future high-tech lifestyles by guaranteeing the perpetual operation of trillions of low-power IoT devices. Currently, reconfigurable intelligent surface (RIS) is a promising technology for achieving cost-effective and energy-efficient wireless technologies. In this paper, we propose an efficient beam-sharing algorithm for RIS-aided SWIPT systems.

Generalized Approximate Message Passing (GAMP) allows for Bayesian inference in linear models with non-identically independently distributed (n.i.i.d.) priors and n.i.i.d. measurements of the linear mixture outputs. It represents an efficient technique for approximate inference, which becomes accurate when both rows and columns of the measurement matrix can be treated as sets of independent vectors and both dimensions become large.

We consider solving ill-posed imaging inverse problems without access to an explicit image prior or ground-truth examples. An overarching challenge in inverse problems is that there are many undesired images that fit to the observed measurements, thus requiring image priors to constrain the space of possible solutions to more plausible reconstructions. However, in many applications it is difficult or potentially impossible to obtain ground-truth images to learn an image prior. Thus, inaccurate priors are often used, which inevitably result in biased solutions.

Melody harmonization has long been closely associated with chorales composed by Johann Sebastian Bach. Previous works rarely emphasised chorale generation conditioned on chord progressions, and there has been a lack of focus on assistive compositional tools. In this paper, we first designed a music representation that encoded chord symbols for chord conditioning, and then proposed DeepChoir, a melody harmonization system that can generate a four-part chorale for a given melody conditioned on a chord progression.

We would like to present Nkululeko, a template based system that lets users perform machine learning experiments in the speaker characteristics domain. It is mainly targeted on users not being familiar with machine learning, or computer programming at all, to being used as a teaching tool or a simple entry level tool to the field of artificial intelligence.

In this study, we propose a dense frequency-time attentive network (DeFT-AN) for multichannel speech enhancement. DeFT-AN is a mask estimation network that predicts a complex spectral masking pattern for suppressing the noise and reverberation embedded in the short-time Fourier transform (STFT) of an input signal. The proposed mask estimation network incorporates three different types of blocks for aggregating information in the spatial, spectral, and temporal dimensions.

We present a variational message passing (VMP)-based approach to detect the presence of a person based on their respiratory chest motion using multistatic ultra-wideband (UWB) radar. In the process, the respiratory motion is estimated for contact-free vital sign monitoring. The received signal is modeled as a backscatter channel and the respiratory motion and propagation channels are estimated using VMP. We use the evidence lower bound (ELBO) to approximate the model evidence for the detection.