Utilizing fine grained analysis of wireless signals for human activity recognition has gained a lot of traction recently. The unique changes to the ambient wireless signals caused by different activities made it possible to recognize these fingerprints through deep learning classification methods. Most of the existing work consider a set of physical activities or gestures and try to recognize each one of them as a separate class. However, this makes the classification task harder especially when the number of activities to recognize becomes larger and when these activities include movements from the same body parts. To address that, in this study, we consider the decomposition of each physical activity into the limbs and body parts involved in that activity and study a one-by-one recognition solution. We propose a Generative Adversarial Network (GAN)-based hierarchical method that not only recognizes the involved body limbs and facilitates the recognition of complex activities, but also mitigates the temporal effects in the collected signal data and thus provides a generalized solution. Our experimental evaluation shows that we can recognize unknown physical activities through the proposed hierarchical limb recognition based model with a small Hamming loss and by just using WiFi signal data from a single transmitter and receiver link.

Summary: Data visualization is often regarded as a post hoc step for verifying statistically significant results in the analysis of high-throughput datasets. This common practice leaves a large amount of raw data behind, from which more information can be extracted. However, existing solutions do not provide capabilities to explore large-scale raw datasets using biologically sensible queries, nor do they allow user interaction based real-time customization of graphics. To address these drawbacks, we have designed an open-source, web-based tool called Systems-Level Interactive Data Exploration, or SLIDE to visualize large-scale -omics data interactively. SLIDE's interface makes it easier for scientists to explore quantitative expression data in multiple resolutions in a single screen. Availability and implementation: SLIDE is publicly available under BSD license both as an online version as well as a stand-alone version at https://github.com/soumitag/SLIDE. Supplementary information: Supplementary data are available at Bioinformatics online.

Bead encoding is a key problem central to all bead based microarrays. Recently a spatially addressable bead encoding technique has been developed ([1], [2]) that alleviates the need for costly hardware while still allowing high-throughput analysis. This paper proposes a pattern matching based scheme that extends this bead encoding technique's usability to uncontrolled environments. A novel affine invariant point pattern matching algorithm is developed to achieve this. The proposed algorithm uses local features to overcome the combinatorial explosion problem encountered in matching corrupted point patterns. The use of efficient data structures is emphasized to make the algorithm fast and scalable. The proposed scheme can decode bead identities in assays involving thousands of beads in a few seconds. Evaluation results using both real and simulated data are presented.